{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、文本特征提取\n",
    "\n",
    "\n",
    "## 1、词向量\n",
    "\n",
    "\n",
    "### 1.1、维基百科中文\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of list\n",
    "\n",
    "model = word2vec.Word2Vec(sentences, min_count=1)\n",
    "\n",
    "\n",
    "model = Word2Vec.load('wiki.zh.text.model')\n",
    "\n",
    "\n",
    "model.similarity('dig','dog')\n",
    "res = model.most_similar(testword)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2、影评情感分类\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import nltk\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reviews: 25000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5814_8</td>\n",
       "      <td>1</td>\n",
       "      <td>With all this stuff going down at the moment w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2381_9</td>\n",
       "      <td>1</td>\n",
       "      <td>\"The Classic War of the Worlds\" by Timothy Hin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7759_3</td>\n",
       "      <td>0</td>\n",
       "      <td>The film starts with a manager (Nicholas Bell)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3630_4</td>\n",
       "      <td>0</td>\n",
       "      <td>It must be assumed that those who praised this...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9495_8</td>\n",
       "      <td>1</td>\n",
       "      <td>Superbly trashy and wondrously unpretentious 8...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  sentiment                                             review\n",
       "0  5814_8          1  With all this stuff going down at the moment w...\n",
       "1  2381_9          1  \"The Classic War of the Worlds\" by Timothy Hin...\n",
       "2  7759_3          0  The film starts with a manager (Nicholas Bell)...\n",
       "3  3630_4          0  It must be assumed that those who praised this...\n",
       "4  9495_8          1  Superbly trashy and wondrously unpretentious 8..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/IV/labeledTrainData.tsv', sep='\\t', escapechar='\\\\')\n",
    "print('Number of reviews: {}'.format(len(df)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"I watched this movie really late last night and usually if it's late then I'm pretty forgiving of movies. Although I tried, I just could not stand this movie at all, it kept getting worse and worse as the movie went on. Although I know it's suppose to be a comedy but I didn't find it very funny. It was also an especially unrealistic, and jaded portrayal of rural life. In case this is what any of you think country life is like, it's definitely not. I do have to agree that some of the guy cast members were cute, but the french guy was really fake. I do have to agree that it tried to have a good lesson in the story, but overall my recommendation is that no one over 8 watch it, it's just too annoying.\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#去掉HTML标签的数据\n",
    "example = BeautifulSoup(df['review'][1000], 'html.parser').get_text()\n",
    "example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I watched this movie really late last night and usually if it s late then I m pretty forgiving of movies  Although I tried  I just could not stand this movie at all  it kept getting worse and worse as the movie went on  Although I know it s suppose to be a comedy but I didn t find it very funny  It was also an especially unrealistic  and jaded portrayal of rural life  In case this is what any of you think country life is like  it s definitely not  I do have to agree that some of the guy cast members were cute  but the french guy was really fake  I do have to agree that it tried to have a good lesson in the story  but overall my recommendation is that no one over   watch it  it s just too annoying '"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#去掉标点符号\n",
    "example_letters = re.sub(r'[^a-zA-Z]', ' ', example)\n",
    "example_letters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'watched',\n",
       " 'this',\n",
       " 'movie',\n",
       " 'really',\n",
       " 'late',\n",
       " 'last',\n",
       " 'night',\n",
       " 'and',\n",
       " 'usually',\n",
       " 'if',\n",
       " 'it',\n",
       " 's',\n",
       " 'late',\n",
       " 'then',\n",
       " 'i',\n",
       " 'm',\n",
       " 'pretty',\n",
       " 'forgiving',\n",
       " 'of',\n",
       " 'movies',\n",
       " 'although',\n",
       " 'i',\n",
       " 'tried',\n",
       " 'i',\n",
       " 'just',\n",
       " 'could',\n",
       " 'not',\n",
       " 'stand',\n",
       " 'this',\n",
       " 'movie',\n",
       " 'at',\n",
       " 'all',\n",
       " 'it',\n",
       " 'kept',\n",
       " 'getting',\n",
       " 'worse',\n",
       " 'and',\n",
       " 'worse',\n",
       " 'as',\n",
       " 'the',\n",
       " 'movie',\n",
       " 'went',\n",
       " 'on',\n",
       " 'although',\n",
       " 'i',\n",
       " 'know',\n",
       " 'it',\n",
       " 's',\n",
       " 'suppose',\n",
       " 'to',\n",
       " 'be',\n",
       " 'a',\n",
       " 'comedy',\n",
       " 'but',\n",
       " 'i',\n",
       " 'didn',\n",
       " 't',\n",
       " 'find',\n",
       " 'it',\n",
       " 'very',\n",
       " 'funny',\n",
       " 'it',\n",
       " 'was',\n",
       " 'also',\n",
       " 'an',\n",
       " 'especially',\n",
       " 'unrealistic',\n",
       " 'and',\n",
       " 'jaded',\n",
       " 'portrayal',\n",
       " 'of',\n",
       " 'rural',\n",
       " 'life',\n",
       " 'in',\n",
       " 'case',\n",
       " 'this',\n",
       " 'is',\n",
       " 'what',\n",
       " 'any',\n",
       " 'of',\n",
       " 'you',\n",
       " 'think',\n",
       " 'country',\n",
       " 'life',\n",
       " 'is',\n",
       " 'like',\n",
       " 'it',\n",
       " 's',\n",
       " 'definitely',\n",
       " 'not',\n",
       " 'i',\n",
       " 'do',\n",
       " 'have',\n",
       " 'to',\n",
       " 'agree',\n",
       " 'that',\n",
       " 'some',\n",
       " 'of',\n",
       " 'the',\n",
       " 'guy',\n",
       " 'cast',\n",
       " 'members',\n",
       " 'were',\n",
       " 'cute',\n",
       " 'but',\n",
       " 'the',\n",
       " 'french',\n",
       " 'guy',\n",
       " 'was',\n",
       " 'really',\n",
       " 'fake',\n",
       " 'i',\n",
       " 'do',\n",
       " 'have',\n",
       " 'to',\n",
       " 'agree',\n",
       " 'that',\n",
       " 'it',\n",
       " 'tried',\n",
       " 'to',\n",
       " 'have',\n",
       " 'a',\n",
       " 'good',\n",
       " 'lesson',\n",
       " 'in',\n",
       " 'the',\n",
       " 'story',\n",
       " 'but',\n",
       " 'overall',\n",
       " 'my',\n",
       " 'recommendation',\n",
       " 'is',\n",
       " 'that',\n",
       " 'no',\n",
       " 'one',\n",
       " 'over',\n",
       " 'watch',\n",
       " 'it',\n",
       " 'it',\n",
       " 's',\n",
       " 'just',\n",
       " 'too',\n",
       " 'annoying']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = example_letters.lower().split()\n",
    "words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['watched',\n",
       " 'movie',\n",
       " 'late',\n",
       " 'night',\n",
       " 'late',\n",
       " 'pretty',\n",
       " 'forgiving',\n",
       " 'movies',\n",
       " 'stand',\n",
       " 'movie',\n",
       " 'worse',\n",
       " 'worse',\n",
       " 'movie',\n",
       " 'suppose',\n",
       " 'comedy',\n",
       " 'didn',\n",
       " 'funny',\n",
       " 'unrealistic',\n",
       " 'jaded',\n",
       " 'portrayal',\n",
       " 'rural',\n",
       " 'life',\n",
       " 'country',\n",
       " 'life',\n",
       " 'agree',\n",
       " 'guy',\n",
       " 'cast',\n",
       " 'cute',\n",
       " 'french',\n",
       " 'guy',\n",
       " 'fake',\n",
       " 'agree',\n",
       " 'lesson',\n",
       " 'story',\n",
       " 'recommendation',\n",
       " 'watch',\n",
       " 'annoying']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#去停用词\n",
    "stopwords = {}.fromkeys([ line.rstrip() for line in open('./data/IV/stopwords.txt')])\n",
    "words_nostop = [w for w in words if w not in stopwords]\n",
    "words_nostop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng_stopwords = set(stopwords)\n",
    "\n",
    "# 封装\n",
    "def clean_text(text):\n",
    "    text = BeautifulSoup(text, 'html.parser').get_text()\n",
    "    text = re.sub(r'[^a-zA-Z]', ' ', text)\n",
    "    words = text.lower().split()\n",
    "    words = [w for w in words if w not in eng_stopwords]\n",
    "    return ' '.join(words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>review</th>\n",
       "      <th>clean_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5814_8</td>\n",
       "      <td>1</td>\n",
       "      <td>With all this stuff going down at the moment w...</td>\n",
       "      <td>stuff moment mj ve started listening music wat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2381_9</td>\n",
       "      <td>1</td>\n",
       "      <td>\"The Classic War of the Worlds\" by Timothy Hin...</td>\n",
       "      <td>classic war worlds timothy hines entertaining ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7759_3</td>\n",
       "      <td>0</td>\n",
       "      <td>The film starts with a manager (Nicholas Bell)...</td>\n",
       "      <td>film starts manager nicholas bell investors ro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3630_4</td>\n",
       "      <td>0</td>\n",
       "      <td>It must be assumed that those who praised this...</td>\n",
       "      <td>assumed praised film filmed opera didn read do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9495_8</td>\n",
       "      <td>1</td>\n",
       "      <td>Superbly trashy and wondrously unpretentious 8...</td>\n",
       "      <td>superbly trashy wondrously unpretentious explo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  sentiment                                             review  \\\n",
       "0  5814_8          1  With all this stuff going down at the moment w...   \n",
       "1  2381_9          1  \"The Classic War of the Worlds\" by Timothy Hin...   \n",
       "2  7759_3          0  The film starts with a manager (Nicholas Bell)...   \n",
       "3  3630_4          0  It must be assumed that those who praised this...   \n",
       "4  9495_8          1  Superbly trashy and wondrously unpretentious 8...   \n",
       "\n",
       "                                        clean_review  \n",
       "0  stuff moment mj ve started listening music wat...  \n",
       "1  classic war worlds timothy hines entertaining ...  \n",
       "2  film starts manager nicholas bell investors ro...  \n",
       "3  assumed praised film filmed opera didn read do...  \n",
       "4  superbly trashy wondrously unpretentious explo...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 清洗数据添加到dataframe里\n",
    "df['clean_review'] = df.review.apply(clean_text)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    方法一：抽取bag of words特征(用sklearn的CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25000, 5000)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(max_features = 5000) \n",
    "train_data_features = vectorizer.fit_transform(df.clean_review).toarray()\n",
    "train_data_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data_features,df.sentiment,test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 画图\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=0)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/apple/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset:  0.8531810766721044\n",
      "accuracy metric in the testing dataset:  0.8454\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练分类器LR\n",
    "LR_model = LogisticRegression()\n",
    "LR_model = LR_model.fit(X_train, y_train)\n",
    "y_pred = LR_model.predict(X_test)\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred)\n",
    "\n",
    "print(\"Recall metric in the testing dataset: \", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))\n",
    "\n",
    "print(\"accuracy metric in the testing dataset: \", (cnf_matrix[1,1]+cnf_matrix[0,0])/(cnf_matrix[0,0]+cnf_matrix[1,1]+cnf_matrix[1,0]+cnf_matrix[0,1]))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "    方法二：word2vec特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reviews: 50000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9999_0</td>\n",
       "      <td>Watching Time Chasers, it obvious that it was ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45057_0</td>\n",
       "      <td>I saw this film about 20 years ago and remembe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15561_0</td>\n",
       "      <td>Minor Spoilers&lt;br /&gt;&lt;br /&gt;In New York, Joan Ba...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7161_0</td>\n",
       "      <td>I went to see this film with a great deal of e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43971_0</td>\n",
       "      <td>Yes, I agree with everyone on this site this m...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                             review\n",
       "0   9999_0  Watching Time Chasers, it obvious that it was ...\n",
       "1  45057_0  I saw this film about 20 years ago and remembe...\n",
       "2  15561_0  Minor Spoilers<br /><br />In New York, Joan Ba...\n",
       "3   7161_0  I went to see this film with a great deal of e...\n",
       "4  43971_0  Yes, I agree with everyone on this site this m..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/IV/unlabeledTrainData.tsv', sep='\\t', escapechar='\\\\')\n",
    "print('Number of reviews: {}'.format(len(df)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>review</th>\n",
       "      <th>clean_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9999_0</td>\n",
       "      <td>Watching Time Chasers, it obvious that it was ...</td>\n",
       "      <td>watching time chasers obvious bunch friends si...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45057_0</td>\n",
       "      <td>I saw this film about 20 years ago and remembe...</td>\n",
       "      <td>film ago remember nasty based true incident br...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15561_0</td>\n",
       "      <td>Minor Spoilers&lt;br /&gt;&lt;br /&gt;In New York, Joan Ba...</td>\n",
       "      <td>minor spoilersin york joan barnard elvire audr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7161_0</td>\n",
       "      <td>I went to see this film with a great deal of e...</td>\n",
       "      <td>film deal excitement school director friend mi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43971_0</td>\n",
       "      <td>Yes, I agree with everyone on this site this m...</td>\n",
       "      <td>agree site movie bad call movie insult movies ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                             review  \\\n",
       "0   9999_0  Watching Time Chasers, it obvious that it was ...   \n",
       "1  45057_0  I saw this film about 20 years ago and remembe...   \n",
       "2  15561_0  Minor Spoilers<br /><br />In New York, Joan Ba...   \n",
       "3   7161_0  I went to see this film with a great deal of e...   \n",
       "4  43971_0  Yes, I agree with everyone on this site this m...   \n",
       "\n",
       "                                        clean_review  \n",
       "0  watching time chasers obvious bunch friends si...  \n",
       "1  film ago remember nasty based true incident br...  \n",
       "2  minor spoilersin york joan barnard elvire audr...  \n",
       "3  film deal excitement school director friend mi...  \n",
       "4  agree site movie bad call movie insult movies ...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['clean_review'] = df.review.apply(clean_text)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_part = df['clean_review']\n",
    "review_part.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50000 reviews -> 50000 sentences\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n",
    "\n",
    "\n",
    "def split_sentences(review):\n",
    "    raw_sentences = tokenizer.tokenize(review.strip())\n",
    "    sentences = [clean_text(s) for s in raw_sentences if s]\n",
    "    return sentences\n",
    "sentences = sum(review_part.apply(split_sentences), [])\n",
    "print('{} reviews -> {} sentences'.format(len(review_part), len(sentences)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "watching time chasers obvious bunch friends sitting day film school hey pool money bad movie bad movie dull story bad script lame acting poor cinematography bottom barrel stock music corners cut prevented film release life\n"
     ]
    }
   ],
   "source": [
    "print(sentences[0])\n",
    "\n",
    "# 分词\n",
    "sentences_list = []\n",
    "for line in sentences:\n",
    "    sentences_list.append(nltk.word_tokenize(line))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  sentences：可以是一个list\n",
    "-  sg： 用于设置训练算法，默认为0，对应CBOW算法；sg=1则采用skip-gram算法。\n",
    "-  size：是指特征向量的维度，默认为100。大的size需要更多的训练数据,但是效果会更好. 推荐值为几十到几百。\n",
    "-  window：表示当前词与预测词在一个句子中的最大距离是多少\n",
    "-  alpha: 是学习速率\n",
    "-  seed：用于随机数发生器。与初始化词向量有关。\n",
    "-  min_count: 可以对字典做截断. 词频少于min_count次数的单词会被丢弃掉, 默认值为5\n",
    "-  max_vocab_size: 设置词向量构建期间的RAM限制。如果所有独立单词个数超过这个，则就消除掉其中最不频繁的一个。每一千万个单词需要大约1GB的RAM。设置成None则没有限制。\n",
    "\n",
    "-  workers参数控制训练的并行数。\n",
    "-  hs: 如果为1则会采用hierarchica·softmax技巧。如果设置为0（defau·t），则negative sampling会被使用。\n",
    "-  negative: 如果>0,则会采用negativesamp·ing，用于设置多少个noise words\n",
    "-  iter： 迭代次数，默认为5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设定词向量训练的参数\n",
    "num_features = 300    # Word vector dimensionality\n",
    "min_word_count = 40   # Minimum word count\n",
    "num_workers = 4       # Number of threads to run in parallel\n",
    "context = 10          # Context window size\n",
    "model_name = '{}features_{}minwords_{}context.model'.format(num_features, min_word_count, context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练模型\n",
    "from gensim.models.word2vec import Word2Vec\n",
    "model = Word2Vec(sentences_list, workers=num_workers, \\\n",
    "            size=num_features, min_count = min_word_count, \\\n",
    "            window = context)\n",
    "\n",
    "# If you don't plan to train the model any further, calling \n",
    "# init_sims will make the model much more memory-efficient.\n",
    "model.init_sims(replace=True)\n",
    "\n",
    "# It can be helpful to create a meaningful model name and \n",
    "# save the model for later use. You can load it later using Word2Vec.load()\n",
    "# model.save(os.path.join('..', 'models', model_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kitchen\n"
     ]
    }
   ],
   "source": [
    "# 测试1\n",
    "print(model.doesnt_match(['man','woman','child','kitchen']))\n",
    "#print(model.doesnt_match('france england germany berlin'.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('girl', 0.6982134580612183),\n",
       " ('astro', 0.6635538339614868),\n",
       " ('dad', 0.6197444796562195),\n",
       " ('teenage', 0.618891716003418),\n",
       " ('kid', 0.6153948307037354),\n",
       " ('yr', 0.6033892035484314),\n",
       " ('teenager', 0.5904064178466797),\n",
       " ('orphan', 0.5852910280227661),\n",
       " ('father', 0.5747910737991333),\n",
       " ('joshua', 0.574756383895874)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试2\n",
    "model.most_similar(\"boy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 开始同上面比较\n",
    "df = pd.read_csv('./data/IV/labeledTrainData.tsv', sep='\\t', escapechar='\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.036186</td>\n",
       "      <td>-0.418601</td>\n",
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       "      <td>1.855283</td>\n",
       "      <td>0.732842</td>\n",
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       "      <td>-2.826517</td>\n",
       "      <td>-2.227916</td>\n",
       "      <td>-0.287209</td>\n",
       "      <td>-1.277576</td>\n",
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       "      <td>-0.194151</td>\n",
       "      <td>-0.035671</td>\n",
       "      <td>2.855557</td>\n",
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       "      <td>5.226791</td>\n",
       "      <td>-0.879574</td>\n",
       "      <td>2.226658</td>\n",
       "      <td>-0.684817</td>\n",
       "      <td>-0.076152</td>\n",
       "      <td>1.861919</td>\n",
       "      <td>1.518197</td>\n",
       "      <td>-2.064505</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.367116</td>\n",
       "      <td>0.261728</td>\n",
       "      <td>-1.709851</td>\n",
       "      <td>-0.255335</td>\n",
       "      <td>-1.111785</td>\n",
       "      <td>6.469168</td>\n",
       "      <td>3.201819</td>\n",
       "      <td>1.483777</td>\n",
       "      <td>-2.503048</td>\n",
       "      <td>-2.030492</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>0.121515</td>\n",
       "      <td>-2.434086</td>\n",
       "      <td>1.592252</td>\n",
       "      <td>0.212761</td>\n",
       "      <td>-1.274822</td>\n",
       "      <td>-0.873114</td>\n",
       "      <td>-1.595557</td>\n",
       "      <td>-1.082095</td>\n",
       "      <td>1.228057</td>\n",
       "      <td>-0.592284</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.816655</td>\n",
       "      <td>-1.587449</td>\n",
       "      <td>2.302938</td>\n",
       "      <td>-1.988097</td>\n",
       "      <td>2.995237</td>\n",
       "      <td>1.582491</td>\n",
       "      <td>1.071297</td>\n",
       "      <td>-3.418438</td>\n",
       "      <td>-2.014541</td>\n",
       "      <td>-2.109902</td>\n",
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       "      <td>-1.297425</td>\n",
       "      <td>2.914258</td>\n",
       "      <td>1.418531</td>\n",
       "      <td>-0.416321</td>\n",
       "      <td>-0.280445</td>\n",
       "      <td>0.627747</td>\n",
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       "      <td>-1.840300</td>\n",
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       "      <td>-1.171067</td>\n",
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       "      <td>1.220308</td>\n",
       "      <td>4.343326</td>\n",
       "      <td>-0.026972</td>\n",
       "      <td>1.126408</td>\n",
       "      <td>-0.110923</td>\n",
       "      <td>-2.420944</td>\n",
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       "        0         1         2         3         4         5         6    \\\n",
       "0  0.331600 -0.877856  3.064303  0.548345  2.071364 -0.789658 -2.488780   \n",
       "1  0.010377 -0.818570  1.855283  0.732842  0.171763 -1.116322 -2.826517   \n",
       "2  3.500354  1.108565  5.226791 -0.879574  2.226658 -0.684817 -0.076152   \n",
       "3  0.121515 -2.434086  1.592252  0.212761 -1.274822 -0.873114 -1.595557   \n",
       "4  1.634280 -1.297425  2.914258  1.418531 -0.416321 -0.280445  0.627747   \n",
       "\n",
       "        7         8         9    ...       290       291       292       293  \\\n",
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       "1 -2.227916 -0.287209 -1.277576  ... -0.194151 -0.035671  2.855557  1.681300   \n",
       "2  1.861919  1.518197 -2.064505  ... -0.367116  0.261728 -1.709851 -0.255335   \n",
       "3 -1.082095  1.228057 -0.592284  ... -0.816655 -1.587449  2.302938 -1.988097   \n",
       "4  1.714436  0.025170 -1.840300  ... -0.334547 -2.447309 -1.171067 -2.522420   \n",
       "\n",
       "        294       295       296       297       298       299  \n",
       "0  1.592740  1.330478 -0.693583 -0.055844  1.036186 -0.418601  \n",
       "1  1.057660 -0.435652 -0.093196  0.120307  0.721767  0.424219  \n",
       "2 -1.111785  6.469168  3.201819  1.483777 -2.503048 -2.030492  \n",
       "3  2.995237  1.582491  1.071297 -3.418438 -2.014541 -2.109902  \n",
       "4  1.220308  4.343326 -0.026972  1.126408 -0.110923 -2.420944  \n",
       "\n",
       "[5 rows x 300 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "eng_stopwords = set(stopwords.words('english'))\n",
    "\n",
    "def clean_text(text, remove_stopwords=False):\n",
    "    text = BeautifulSoup(text, 'html.parser').get_text()\n",
    "    text = re.sub(r'[^a-zA-Z]', ' ', text)\n",
    "    words = text.lower().split()\n",
    "    if remove_stopwords:\n",
    "        words = [w for w in words if w not in eng_stopwords]\n",
    "    return words\n",
    "\n",
    "def to_review_vector(review):\n",
    "    global word_vec\n",
    "    \n",
    "    review = clean_text(review, remove_stopwords=True)\n",
    "    #print (review)\n",
    "    #words = nltk.word_tokenize(review)\n",
    "    word_vec = np.zeros((1,300))\n",
    "    for word in review:\n",
    "        #word_vec = np.zeros((1,300))\n",
    "        if word in model:\n",
    "            word_vec += np.array([model[word]])\n",
    "    #print (word_vec.mean(axis = 0))\n",
    "    return pd.Series(word_vec.mean(axis = 0))\n",
    "\n",
    "train_data_features = df.review.apply(to_review_vector)\n",
    "train_data_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data_features,df.sentiment,test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset:  0.8796900489396411\n",
      "accuracy metric in the testing dataset:  0.8656\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LR_model = LogisticRegression()\n",
    "LR_model = LR_model.fit(X_train, y_train)\n",
    "y_pred = LR_model.predict(X_test)\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred)\n",
    "\n",
    "print(\"Recall metric in the testing dataset: \", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))\n",
    "\n",
    "print(\"accuracy metric in the testing dataset: \", (cnf_matrix[1,1]+cnf_matrix[0,0])/(cnf_matrix[0,0]+cnf_matrix[1,1]+cnf_matrix[1,0]+cnf_matrix[0,1]))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、推特灾难事件检测\n",
    "\n",
    "\n",
    "### 2.1、简介\n",
    "\n",
    "    Disasters on social media：社交媒体上有些讨论是关于灾难，疾病，暴乱的，有些只是开玩笑或者是电影情节，我们该如何让机器能分辨出这两种讨论呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "import nltk\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import codecs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>choose_one</th>\n",
       "      <th>class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Just happened a terrible car crash</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heard about #earthquake is different cities, s...</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>there is a forest fire at spot pond, geese are...</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text choose_one  class_label\n",
       "0                 Just happened a terrible car crash   Relevant            1\n",
       "1  Our Deeds are the Reason of this #earthquake M...   Relevant            1\n",
       "2  Heard about #earthquake is different cities, s...   Relevant            1\n",
       "3  there is a forest fire at spot pond, geese are...   Relevant            1\n",
       "4             Forest fire near La Ronge Sask. Canada   Relevant            1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions = pd.read_csv(\"./data/IV/socialmedia_relevant_cols_clean.csv\")\n",
    "questions.columns=['text', 'choose_one', 'class_label']\n",
    "questions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>choose_one</th>\n",
       "      <th>class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>just happened a terrible car crash</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>our deeds are the reason of this  earthquake m...</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>heard about  earthquake is different cities, s...</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>there is a forest fire at spot pond, geese are...</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>forest fire near la ronge sask  canada</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text choose_one  class_label\n",
       "0                 just happened a terrible car crash   Relevant            1\n",
       "1  our deeds are the reason of this  earthquake m...   Relevant            1\n",
       "2  heard about  earthquake is different cities, s...   Relevant            1\n",
       "3  there is a forest fire at spot pond, geese are...   Relevant            1\n",
       "4             forest fire near la ronge sask  canada   Relevant            1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据清洗，去掉无用字符\n",
    "def standardize_text(df, text_field):\n",
    "    df[text_field] = df[text_field].str.replace(r\"http\\S+\", \"\")\n",
    "    df[text_field] = df[text_field].str.replace(r\"http\", \"\")\n",
    "    df[text_field] = df[text_field].str.replace(r\"@\\S+\", \"\")\n",
    "    df[text_field] = df[text_field].str.replace(r\"[^A-Za-z0-9(),!?@\\'\\`\\\"\\_\\n]\", \" \")\n",
    "    df[text_field] = df[text_field].str.replace(r\"@\", \"at\")\n",
    "    df[text_field] = df[text_field].str.lower()\n",
    "    return df\n",
    "\n",
    "questions = standardize_text(questions, \"text\")\n",
    "\n",
    "questions.to_csv(\"./data/IV/clean_data.csv\", index = False)\n",
    "questions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读入\n",
    "clean_questions = pd.read_csv(\"./data/IV/clean_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>choose_one</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6187</td>\n",
       "      <td>6187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4673</td>\n",
       "      <td>4673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             text  choose_one\n",
       "class_label                  \n",
       "0            6187        6187\n",
       "1            4673        4673\n",
       "2              16          16"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据分布情况：是否倾斜\n",
    "clean_questions.groupby(\"class_label\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>choose_one</th>\n",
       "      <th>class_label</th>\n",
       "      <th>tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>just happened a terrible car crash</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "      <td>[just, happened, a, terrible, car, crash]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>our deeds are the reason of this  earthquake m...</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "      <td>[our, deeds, are, the, reason, of, this, earth...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>heard about  earthquake is different cities, s...</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "      <td>[heard, about, earthquake, is, different, citi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>there is a forest fire at spot pond, geese are...</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "      <td>[there, is, a, forest, fire, at, spot, pond, g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>forest fire near la ronge sask  canada</td>\n",
       "      <td>Relevant</td>\n",
       "      <td>1</td>\n",
       "      <td>[forest, fire, near, la, ronge, sask, canada]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text choose_one  class_label  \\\n",
       "0                 just happened a terrible car crash   Relevant            1   \n",
       "1  our deeds are the reason of this  earthquake m...   Relevant            1   \n",
       "2  heard about  earthquake is different cities, s...   Relevant            1   \n",
       "3  there is a forest fire at spot pond, geese are...   Relevant            1   \n",
       "4             forest fire near la ronge sask  canada   Relevant            1   \n",
       "\n",
       "                                              tokens  \n",
       "0          [just, happened, a, terrible, car, crash]  \n",
       "1  [our, deeds, are, the, reason, of, this, earth...  \n",
       "2  [heard, about, earthquake, is, different, citi...  \n",
       "3  [there, is, a, forest, fire, at, spot, pond, g...  \n",
       "4      [forest, fire, near, la, ronge, sask, canada]  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分词\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "\n",
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "\n",
    "clean_questions[\"tokens\"] = clean_questions[\"text\"].apply(tokenizer.tokenize)\n",
    "clean_questions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "154724 words total, with a vocabulary size of 18101\n",
      "Max sentence length is 34\n"
     ]
    }
   ],
   "source": [
    "# 语料库情况\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "all_words = [word for tokens in clean_questions[\"tokens\"] for word in tokens]\n",
    "sentence_lengths = [len(tokens) for tokens in clean_questions[\"tokens\"]]\n",
    "VOCAB = sorted(list(set(all_words)))\n",
    "print(\"%s words total, with a vocabulary size of %s\" % (len(all_words), len(VOCAB)))\n",
    "print(\"Max sentence length is %s\" % max(sentence_lengths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 句子长度情况\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(5, 5)) \n",
    "plt.xlabel('Sentence length')\n",
    "plt.ylabel('Number of sentences')\n",
    "plt.hist(sentence_lengths)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2、Bag of Words Counts 词袋模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "def cv(data):\n",
    "    count_vectorizer = CountVectorizer()\n",
    "\n",
    "    emb = count_vectorizer.fit_transform(data)\n",
    "\n",
    "    return emb, count_vectorizer\n",
    "\n",
    "list_corpus = clean_questions[\"text\"].tolist()\n",
    "list_labels = clean_questions[\"class_label\"].tolist()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(list_corpus, list_labels, test_size=0.2, \n",
    "                                                                                random_state=40)\n",
    "\n",
    "X_train_counts, count_vectorizer = cv(X_train)\n",
    "X_test_counts = count_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图：PCA展示Bag of Words\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "import matplotlib\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "\n",
    "def plot_LSA(test_data, test_labels, savepath=\"PCA_demo.csv\", plot=True):\n",
    "        lsa = TruncatedSVD(n_components=2)\n",
    "        lsa.fit(test_data)\n",
    "        lsa_scores = lsa.transform(test_data)\n",
    "        color_mapper = {label:idx for idx,label in enumerate(set(test_labels))}\n",
    "        color_column = [color_mapper[label] for label in test_labels]\n",
    "        colors = ['orange','blue','blue']\n",
    "        if plot:\n",
    "            plt.scatter(lsa_scores[:,0], lsa_scores[:,1], s=8, alpha=.8, c=test_labels, cmap=matplotlib.colors.ListedColormap(colors))\n",
    "            red_patch = mpatches.Patch(color='orange', label='Irrelevant')\n",
    "            green_patch = mpatches.Patch(color='blue', label='Disaster')\n",
    "            plt.legend(handles=[red_patch, green_patch], prop={'size': 30})\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))          \n",
    "plot_LSA(X_train_counts, y_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型：逻辑回归看一下结果\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', \n",
    "                         multi_class='multinomial', n_jobs=-1, random_state=40)\n",
    "clf.fit(X_train_counts, y_train)\n",
    "\n",
    "y_predicted_counts = clf.predict(X_test_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.754, precision = 0.752, recall = 0.754, f1 = 0.753\n"
     ]
    }
   ],
   "source": [
    "# 评估\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report\n",
    "\n",
    "def get_metrics(y_test, y_predicted):  \n",
    "    # true positives / (true positives+false positives)\n",
    "    precision = precision_score(y_test, y_predicted, pos_label=None,\n",
    "                                    average='weighted')             \n",
    "    # true positives / (true positives + false negatives)\n",
    "    recall = recall_score(y_test, y_predicted, pos_label=None,\n",
    "                              average='weighted')\n",
    "    \n",
    "    # harmonic mean of precision and recall\n",
    "    f1 = f1_score(y_test, y_predicted, pos_label=None, average='weighted')\n",
    "    \n",
    "    # true positives + true negatives/ total\n",
    "    accuracy = accuracy_score(y_test, y_predicted)\n",
    "    return accuracy, precision, recall, f1\n",
    "\n",
    "accuracy, precision, recall, f1 = get_metrics(y_test, y_predicted_counts)\n",
    "print(\"accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 混淆矩阵检查\n",
    "import numpy as np\n",
    "import itertools\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap='Blues'):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=30)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, fontsize=20)\n",
    "    plt.yticks(tick_marks, classes, fontsize=20)\n",
    "    \n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", \n",
    "                 color=\"gray\" if cm[i, j] < thresh else \"white\", fontsize=40)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label', fontsize=16)\n",
    "    plt.xlabel('Predicted label', fontsize=16)\n",
    "\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[970 251   3]\n",
      " [274 670   1]\n",
      " [  3   4   0]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_predicted_counts)\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plot = plot_confusion_matrix(cm, classes=['Irrelevant','Disaster','Unsure'], normalize=False, title='Confusion matrix')\n",
    "plt.show()\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 进一步检查模型的关注点\n",
    "def get_most_important_features(vectorizer, model, n=5):\n",
    "    index_to_word = {v:k for k,v in vectorizer.vocabulary_.items()}\n",
    "    \n",
    "    # loop for each class\n",
    "    classes ={}\n",
    "    for class_index in range(model.coef_.shape[0]):\n",
    "        word_importances = [(el, index_to_word[i]) for i,el in enumerate(model.coef_[class_index])]\n",
    "        sorted_coeff = sorted(word_importances, key = lambda x : x[0], reverse=True)\n",
    "        tops = sorted(sorted_coeff[:n], key = lambda x : x[0])\n",
    "        bottom = sorted_coeff[-n:]\n",
    "        classes[class_index] = {\n",
    "            'tops':tops,\n",
    "            'bottom':bottom\n",
    "        }\n",
    "    return classes\n",
    "\n",
    "importance = get_most_important_features(count_vectorizer, clf, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_important_words(top_scores, top_words, bottom_scores, bottom_words, name):\n",
    "    y_pos = np.arange(len(top_words))\n",
    "    top_pairs = [(a,b) for a,b in zip(top_words, top_scores)]\n",
    "    top_pairs = sorted(top_pairs, key=lambda x: x[1])\n",
    "    \n",
    "    bottom_pairs = [(a,b) for a,b in zip(bottom_words, bottom_scores)]\n",
    "    bottom_pairs = sorted(bottom_pairs, key=lambda x: x[1], reverse=True)\n",
    "    \n",
    "    top_words = [a[0] for a in top_pairs]\n",
    "    top_scores = [a[1] for a in top_pairs]\n",
    "    \n",
    "    bottom_words = [a[0] for a in bottom_pairs]\n",
    "    bottom_scores = [a[1] for a in bottom_pairs]\n",
    "    \n",
    "    fig = plt.figure(figsize=(10, 10))  \n",
    "\n",
    "    plt.subplot(121)\n",
    "    plt.barh(y_pos,bottom_scores, align='center', alpha=0.5)\n",
    "    plt.title('Irrelevant', fontsize=20)\n",
    "    plt.yticks(y_pos, bottom_words, fontsize=14)\n",
    "    plt.suptitle('Key words', fontsize=16)\n",
    "    plt.xlabel('Importance', fontsize=20)\n",
    "    \n",
    "    plt.subplot(122)\n",
    "    plt.barh(y_pos,top_scores, align='center', alpha=0.5)\n",
    "    plt.title('Disaster', fontsize=20)\n",
    "    plt.yticks(y_pos, top_words, fontsize=14)\n",
    "    plt.suptitle(name, fontsize=16)\n",
    "    plt.xlabel('Importance', fontsize=20)\n",
    "    \n",
    "    plt.subplots_adjust(wspace=0.8)\n",
    "    plt.show()\n",
    "\n",
    "top_scores = [a[0] for a in importance[1]['tops']]\n",
    "top_words = [a[1] for a in importance[1]['tops']]\n",
    "bottom_scores = [a[0] for a in importance[1]['bottom']]\n",
    "bottom_words = [a[1] for a in importance[1]['bottom']]\n",
    "\n",
    "plot_important_words(top_scores, top_words, bottom_scores, bottom_words, \"Most important words for relevance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3、TFIDF Bag of Words\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tfidf(data):\n",
    "    tfidf_vectorizer = TfidfVectorizer()\n",
    "\n",
    "    train = tfidf_vectorizer.fit_transform(data)\n",
    "\n",
    "    return train, tfidf_vectorizer\n",
    "\n",
    "X_train_tfidf, tfidf_vectorizer = tfidf(X_train)\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "fig = plt.figure(figsize=(8, 8))          \n",
    "plot_LSA(X_train_tfidf, y_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型\n",
    "clf_tfidf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', \n",
    "                         multi_class='multinomial', n_jobs=-1, random_state=40)\n",
    "clf_tfidf.fit(X_train_tfidf, y_train)\n",
    "\n",
    "y_predicted_tfidf = clf_tfidf.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.762, precision = 0.760, recall = 0.762, f1 = 0.761\n"
     ]
    }
   ],
   "source": [
    "# 评测\n",
    "accuracy_tfidf, precision_tfidf, recall_tfidf, f1_tfidf = get_metrics(y_test, y_predicted_tfidf)\n",
    "print(\"accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy_tfidf, precision_tfidf, \n",
    "                                                                       recall_tfidf, f1_tfidf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BGPMFdhjKBbwtIveJSFzD48Ru8PMcNiPjIurH870W7XwNRmv6QWyWwu3Ub5xkL2qXN/7cuXuviBwdhOurININalTIGWP2ichx2BbpddjxwItEZAd2zfI+7Cztrthu6VOon02+G5ti9R0vZX/jtKjexKbuvFREZmK7qquxm9NMp37i2l+pH4sMtaewGfOOARaJyGfYeQaRwCTsz6EM2xKcwOGrFHphA3IPYIqIvIedOX4CcLJzzB/8WPe+3+mC/wTbslstIvOxAb3Iuc4p1CeJ+Qa4yBhzpCbXtRV3YVdH/xYbQG8Tkc+x2RQjsZMfT8e+h+djP+T+0HhRbnUTF08RkfuwExZfcBI4Bcz5u7sTOzfmSidfwCGMMTNE5CVssH9dRMY3lg0wHIS61R0MGuRVm+Ak4viFiDyN7TI/BxvQvY01b8buI/90c/9wjDGfiMh44I/AxcCFzs3TGuD/jDHBzKPvF2NMuYhMw26C81Ps2Pu5HofMw6bXXee06huev1tsHvLnsUHlJo+ny7AB3p9EQRhjNjutuf8DbsF+iDipwWEHsMMjz4Z7KtTGOBnn7hCRr7DvuXHY35+nHOwY+BPGmBofgsuXzjm9qc8h/yG+L/c7jNgMiHUbAP3RHL5joqdfYz8YDsX2VNwc6HXbrDaQuCYYRPMcqLbImeQzEbtLXBdsq6cY2AmsMMZsb+L0pspNw7Y2+2Kzk+0HFrf1tJ3OGPeJ2H/y2cD3xhhfu9kRkeHYYJwG7AE+N8YcaGGdYp0yh2B3osvDflhaaJrf+azDELv//LHY3qJS7AfUr/3t4RCRHthVEanAXuAdE87bvh5hEWkDTOL0h4JS9sH/XrXMGHPYkNuRoEFeKaVUhxeZNtAknhGcIF/45pUhC/I68U4ppZQKUzomr5RSShGeE++0Ja+UUkqFKW3JK6WUUmhLXimllFKtzEnI9S8RWSgic0TkJBHpJSKficg3IvKmOPshiMjFIrJARL4VkWY399KWvFJKKUVIW/JDsAmJjscuF34Hm5L7HmPMchE5G/g/EfkTNkfBVGxCpP+KyDfGmPXeCtYg34FIZJyRaJ/TlasjaOig3qGugvIiIVr3ZWmrli9flmOM6RrqerSUMWats4fADmwehH8DfYwxy53nPxWR67GZJj8wxlQAiMg/sJkuNcgrkOhOxAy7LNTVUI3419sPN3+QCokJA1JCXQXlRVyU7Gi1wkKY8U5Ezsdu8dsf6AyMBG5vcFgtNpmV5z4HWUC3psrWMXmllFIquLqIyFKP2/UNnp8OPGmMqXX2IzgZm5HTkwv7QcCz56IH9fsaNEqDvFJKKUVQt5rNMcZM9Li90ODSq4HTnDrEANOAOBEZ7Tx2FrAOuxnU+WK3kBbgRuCzpl6TdtcrpZTq8ISQ7v3+EvCciFwFxGM3lZoJvCAi8dj9Jm40xhSJyL+wW0wb4A1jzIamCtYgr5RSSoWQs1vjDY08dVYjx74NvO1r2RrklVJKKTQZjlJKKaXaEW3JK6WUUhCyJXTBpC15pZRSKkxpS14ppZQSHZNXSimlVDuiLXmllFIKbckrpZRSqh3RlrxSSimFtuSVUkop1Y5oS14ppVSHF+Lc9UGjQV4ppZQCTYajlFJKqfZDW/JKKaVUmCbD0SCvWt2wAd2ZMLI/3VM7UWsM+3MO8t3KbezclxfqqgWkR5ck3nvmRo7/yeOhrkqLVZaXUXBgD+WlJdRUVxIdE0dCcipJaT1wuYLTsVdckMOm5fM5auoFfp1XUVZCQfYeKspKqK2tISomjk4pXemU0jUs/xkrFQwa5FWrEBF+cs4k7vr5dAb369boMUvXZHLfcx8zZ/HGZssrW/Fci+u070AhGec+QHlFVYvK+ft9V5DWOaHF9Qmlwtz9bFu1kNysXWDMYc9HRsXQd+gY+o84isjomFa7bm1tDWsWziQyyvcy87P3sHXVIgqy9zT6fEx8IgNGHk3vQSORIH0wCRfV1dW88tI/ycvNPey5G2/5FcmdO4egVm1XOH541L8Q1WIpSfHMeO5mXnzoSq8BHmDiqHQ+++etPHzbeUekXn984fMWB/hrLjyWM08c1Uo1OvKMMWxdtYglX/6P3H07Gw3wANVVFWxfu4TFn/+X4oKcVrv+ttXfU5zvW3mmtpbNKxawbPZ7XgM8QEVpMRuWzGHF3BlUV1a0VlXD0vxv5zYa4FXHoS151SIJcdF8+s9bGT+iLwBl5ZX8e8YiPv1mNTv35REbHcnoYX246rxjOGniEADuuPZ0IiMi+MNTH3gt97WPFvldl/690tzX2LrzAK9++F0Ar6hev54p/Pn2i1pURqit//5r9m5d677fpfcAevQfSkJyKi5XBOWlxeRl7WT3ljXUVFVSVnKQpbPe4+jTLyUhObVF1y7M3c+OdUt9Pn7DkjnsaVjX9GEkJqeCCGVFhezfuYX9OzZijCEvaxcr5s5gwqkX44qIaFFdw1FFRQVlZaWMGjPW/dj2rVsoKSkJYa3atnBsyWuQVy3y3D2XuwP8+m37uOTXz7Nt16Ett1Wb9vDGx4u58rxjePbuHxETHcWvrzqVb5du5vN5axot9/r7X/e7Lu88fYP7+4f/8SnV1bV+l+Hp+Qd+SlJiXIvKCKVdG39wB/iIqGhGH38GXXqlH3JMQnIqaT370X/EUaxe8AX5+3dTXVXBD99+wjFn/STg4FlTU83ahTMxXnoOGtqzZY07wHura2JyGl37DKTvsLGs+vYTKspKKMzJYssP3zH0qBMDqmc4i4mJ4cyzD+01e/O1VzXIdzDaXa8CNiGjHz8+62gA9mYXcNYNzx4W4D299tEibnnkLff9R351fqt9cp40Op1zTh4NwKpNu3n7y2UtKu+Wy6cwZdKw1qhaSFRXVrB1tdMbIsKYE886LGh6io6NZ9yU80ju0gOA0qICdm1eFfD1t6z8jtKD+T4dW1NdxdZVC52qCmNPOrvJuiandWf8KRcQERkFwK5NP1BeUhRwXZVykyDdQkiDvArYT887xv393c/MICvnYLPnvPHxYr5evAGAjEE9mTiyX6vU5cFfnuv+/oHnPvG5BdmYwf268dCtR2beQLDs2bbOPV7dc8AI0no0/3OOiIgk45jTELH/FvZuXRfQtfOz97Br0w8+H5+zN5PK8jJb14EjSO3et9lzEjun0T9jAmDH8rN2ND+ZU6mOSIO8CtiUo4cCUFxawfuzVvh83msz6sfbT3bKaImpk4e7W90LV271OgTgC5dLeOnhK4mPi25xvUIpd2+m+/t+w8Z6P7CBhKQUUrr1BqCkMJeKMv+6dqurKlm3cJbXCX6Nycva7f6+14AMn8/rNXCE+/v8/bubOFIp34hIUG6hpEFeBaxXt2QAtu46QGVVtc/nrdmy1/19724tX8Lz4C/PcX9/77Mftais2685jcljBgCwZ79v3c1tUVH+AQBi4hLolNLVr3MTU7q4v68oLfbr3M0r5lNWctB9bV9UlNVfI6Fzms/Xio3v5F7uV+5nPZVqKFgBXoO8areiIu2krNpa/7rGa2rqJ8RFRrZsVvT5U8cycVQ6AF8uWMuC5VsDLmvk4F7cc8OZ7vs3PfRmi+oWKqa2lqqKcgASkn0PmnXquuvBTqDzVc7eTPZssb0o4nIx/OhTfDrP1Na/H/z9f1j3D9SzDKVUPZ1drwKWk19M356pDOiTRkSE65Dg3ZSh6d3d3+87UBjw9UWE+262rfja2loeeO7jgMuKjHTx0sNXEhNtJ3P9670FzPpufcDlhZQIk6b/CIComFi/Ty8vqZ9bER0b79M5VZUVrF/8lfv+wNGTSezcpYkz6kXF1K9gKD1YQFJa9yaOrldZXub+MONrr4FSTQl1qzsYtCWvArZ83U4AOneKZ/rxvo+l1s3IB1i9yXvSk+Zccc4kMgb1BOD9WStYuSHwcdm7rz+LccPthK8de3P5/ZPvB1xWqIkISWndSUrrTlxisl/n1tbWkJ9tf44RkVHEd/JtOGXj0rnu8fuk1O6kj5jg8zWT0uoTKGXt2OTzeZ7HJvo5JKFUR6FBXgXso7n1S6z++OsL6JTQfKvxtONGcMGp4wAoLatk9qLAWstRkRHcfb3tWq+uruHBv38aUDlglwLece1pgO0RuPHBNygu7ZiZ1LK2b3TPdO/aZ6BPLZvsXVvJyrSz210REYw89jS/0s127T3A3U+/e/MqinzIkFdeWsT2Nd/Xl9FngM/XU8obHZNXysPbXyxl+277D3nYgB6898wNpCR57949ZfIwXvvTz9z335u1nLLywNLOXnfx8QzoY7uD//PRIrbszA6onJjoSF56+Cr33IAX3p7H3O99b02Gk4qyEjavXOC+32/4+GbPqSwvZf33X7vvDxpzrN+Z8uISk+nR366yqK2pYeXcGe6Jg40pKz7IijkzqKooc5+f0rW3X9dUqqPQIK8CVl1dy8/v/Y97Zv2JE4aw/L17+PWVpzK4XzeioyJJSozlhAmDef6Bn/Lx324huZMdf62pqeXxf80M6LpxsVHced10wKbR/ePznwf8Gh6+9TyGD7QJYLbuPMDdz8wIuKz2rLa2ltULvnAHzl6DMkhK9b4PQZ31389xn5PcpadPHwwaM/SoE4mJTwTsh40lX77NxmXfUpi7n+qqSmqqqynKz2HrqkUs+vxNSgrrdzRMHzlRN6pRrSMMk+HoxDvVIt+t3Ma1//dv/vXIVcTGRNGjSxKP3X4hj91+YZPn/XvGwoBb37dcPoWeXe1Y8/Nvz2NPdkFA5Rx/1CBuvnwKYD903PDA65SWVwZUVnu3adk37k1hYhOSGDq++TSx+7av58Buu5rBFRHJyGNOC7hrMjo2nvFTzmfF3BlUlBZTW1vDro0r2bVxZZPnJSSn0nPA8ICuqVRHoB9/VYu9P3sFU699koUrfVu+tj/3IHc//WFA10pOjOM3V08DoLCojP/3cmC9AQlx0bz44JVERNg/gefenMOCFYEvv2vPdqxfzu7NqwFwuSIYfcKZzW43W15azMal37rvDx53PPFJLct5kNg5jUnTf0yPdN+D9oijp+Jy6eY0qnWE45i8tuRVq1ixfhdTr32K0UN7c+rk4fTtmUJEhIt9BwopKa3gz7+9CJfTpXrH4+9SUFQW0HV+c/U0UpPtcqlnXv+KvMLANtv40+0Xucf0N27P4v4WLL9rz7IyN7F5xXz3/eGTTiHZhyVs6xfPprrKTk5M6dabvkPHtEp9YuLiGXXc6Qwedyw5e3dQejCfmuoqomLiSEjqzKbl89zL5noPHk3nbr1a5bpKhSsN8qpVrd6055BlcZGRLua/fqc7wL8/aznvzlweUNldUxK55YopAGTnFfHs63MCKue040bw80tOAOzM/F/c9xoVlb4nfQkXuft2snZRfU9IesYEeg1sfink7s2r7d702GV2GS3opvcmNr4TfQaPOuQxO/5vA3xcQhJDxh/fqtdUHZzoOnml/Pb7n5/B2GF9AMjKOchtf/xf4GX94gwS42038v/715cBLXNLTozjH/dd4b7/5L9ns2TNjoDr1F4V5mSxat6n7kxxPdKHMWjscc2eV1ZceEjLf8hRJxKXmBS0etbJy9rFni2r3fczjplGZFT73l9AqSNBW/IqaMYM7c3vfna6+/5tj75FbkFg3ev9eqZw3cW25bZrXx4vvDO/mTMa99TvL6V39xQA1mzeyyP//CygctqzksI8Vn7zETXVdvliWq90n1rjxhjWLprlPi+1R7/DWtvBUF1VyTqPbHp9h40jpXufoF9XdSyC/2mV2wMN8iooIiNdvPDQlURH2bfYG58s5uO5ge9PfvcNZ7tTzj7y/Gd+bYhT57xTxnD52ZPc90cN6cXBJc/4dG7/XmmUrXjOff+y37zQotcTKuUlRSyf86G72zu5S0/GnHCmezilKTs3rKAgu35zobysncx+868+Xbcge88hx04+83KfN87ZsnKBO9VufFIKg33ocVDKf6GfJBcM2l2vguKu6+q76ffsz+e3f3434LKGpnfnirNtKtyN27N4/ePFfpfRJSWRZ++5POA6hIPK8jKWz/nQvbNcQnIa46acS0RkVLPnlhTmsXXVwmBX8TB5+3e5Z/6LCCOPOY2ISG2bKOUr/WtRrW700N6PiydWAAAgAElEQVTceV19N/2ND75JYXFgs+kBHrjlHHdGugf//onfu94BnDJpGF/OX+v3eVeedwwAxaUVfDB7hfvxXVl53k5pk6qrKlk5dwalB+32ubEJSRx1yvlERfu2gU1e1i669xvq1zVrqivJ3mWXJUbHxpPWs7/7OV+uW1NdxfpF9d30/UdMILlLD7/qoJQ/wrAhH/5BXkSmAHOAAcaYzNDWpvWJiAGuNca8Guq6gNNN/+BP3d30L707n9kLA9/NbdzwPpw/dSwAy9bt5IPZTSdH8eadL5fxzpfL/D6vLsjnFhRz/f2vB3TtUKutqeaHbz/hYJ5NPhQdG8dRp1zgzjDni77Dxvp93bLig+4gH5+UwshjT/PrfM+96RM7pzFw9GS/66BUR3fEu+tF5AERyTzS11VHxl3XneHezW377pwW7+b24C/Pc48X3//sRy2uX0djamtZ/d2X5O+v31lu3JTzW5y4Jtjy9u+u76Z3ucg45jRcEZr0RgVXOCbD0TF51Wo8u+lra2u5/oHXKSkLPE3s8UcN4nRnC9tvlmziq0UbWqWeHcmGJXM44LSmXa4Ixp58jk856UPJdtPPdt8fMHJSm6+zUm1V2HfXqyOjYTf93//7DfOXbWlRmQ/98jz39x01I11LbFm5gD1b6+chxCZ0Yt+2Dezb5vuHpU6pXek3bFwwqueVZzd9Umo30kdOPKLXVx2UhOeYfEhb8iLyqojMFZGTRWS7iBwUkX7eHvc4L05E7hKRH0SkTEQKRWS+iPxcRHx6TSISISI3iMhiESl2bktF5A4RiW1w7DwR8RplROR/IrKkkccvEJGvnPoVO9f6mfPchyLyqsex1zjj64jIMSLyuYgUiEiOiLwuIn0alJ1ZdzzwiogYEXnAl9ceDDdcepK7m37j9izubWHX+unHZ3Dc+EEAfDznBxav2t7iOnYkO9YvJ3PdoXMQSosK2Ld9vV+3um7+I6Uo/8AhefQzjj3Np+V9SqnGtYW/nh7Au8ArwJXGmJ1NPS4iacA84CbgA+DnwO+ALcBzwCwRiWvqgk4Q/xR4GJjrlHUbsBi4D1gsIp6LeJ8EzhaRIY2U1Qe4yDmm7jERkeed+hUBtzv1/Bp4XETewsvPXkSuAGYCG5x6PQGcCiwQkWSPQ68H6rZ6e9b5/q2mXncwvfjufB578XNKyyq5/v7XKa8IbJ/4Ot8s2cQdj7/Lnv35PPC3T1qplh3D3m3rD8lK1550SunKqOOmExUTx6Cxx5KYnBbqKqkOQgCXS4JyC+nrMsb/5UgtuqBtbV5jjEl3WrJXA/cbYx7yOKbRx53nvgCigHONMaUNnhuJDZAfG2NudB6bQoPZ9SLyT+A44FRjzIEGZfQGvgR2G2POcB5zAZuAz40xtzY4/s/AT4B0Y0y189hdwKPAFcaYtxsc3w34AhgNvGGMucZ5/BrsB5oc4HRjzAqPc9KBjcADxpjHGpTn8+x6V3w3EzPssuYOa5G0zgkBZ7VrjMslAS2Za29mv/1wqKvQplRVlhMZFRPySUsAEwakhLoKyou4KFlmjGmV8Zy4nkPNgGufa/7AAKx/bHqr1dNfbaElXwv83ZfHReQkYBq2ZRwtIp09b8Ae4DHgOhFpNKG2EzB/AdwFVDVSRgnwf8B0ERkBYIypBZ4BrvFsTYtIvFPWsx4BPh64B/hLwwDvlJUNXAp4i1x/8gzwzjmZwEfAyV7O8UpErneGIZaa6sDXqvuqNQM80CECvDpcVHRsmwjwqmMRCc4tlNpCkN9pjMnx8fHTgQhgJZDv5fYsdkKht9lC07Cv+7MmypjhHHu0x3kvA1XAdR6PXQ3EAC94PHY8kAi86OX6GGO2YoccGuMtNdx2oKe3Mpu41gvGmInGmIkS2eQohlJKqTDTFmbXNxbgvT3eDVgH3OJDud7Sm3UD8oCLfShjc903xpgSEXkB+KWIPI1tid8GvGqMyfc4p24sfydN2+Pl8f1eHi8DOjVfZaWUUoEIx96jthDkvfUhN/b4ASDFGDO3Bdc7ACQBS4wx/vYtP4sdKjgfqACGOd97yna+9gO2NlFWHxr/IOBt/1RD2+h5UUqp8NMGutaDoS0EjVo/Hp8J9BSRU7wVJiK/EpE/NXG92djX7XUGmohcLCL/FpFDUmwZY/YAbwO/dm6fGmM2NTh9AXAQO/vdW/kDgRMae84c6ZmQSimlwlZbCPI+M8Z8A3wFvCQifRs+LyLnAn8C9jVRxnbgVeAvIjK6kTImY8fY84wxNY0U8QRwEnAa8FQj5ZcBjwC/FZHDPkg4s+sD35JNKaVUq7P7yYdfWtu20F3vryuAWcAPzlr0lUAKMB04Dxugn22mjFuBdOx6+JeAhUACNnj/CLvE7Q+NnWiMWSEi32CHDb72Uv5fgCHA/5x17x9hZ+2Pw87G/wDY5cuL9UEZMEVECoCPnJUASimlVPsL8saYbBE5Fttd/mPnawmwGrjMGPOeD2WUish0bJf6NdgZ81XYBDQ3YyfTNdaKr/ME4DVLh9Plfr2IfA78EtvijwR+AG42xrwtIh82V08fPY39GZyHTSAUeLJ4pZTqsELf6g6GI54MR4XOkUiGowKjyXDaLk2G03a1ZjKc+F7DzJBfNJaypeVWPTQtZMlw2l1LXimllAqGMGzIt6+Jd0oppZTynbbklVJKKcIzGY625JVSSqkwpS15pZRSKkwz3mmQV0op1eHVJcMJN9pdr5RSSoUpbckrpZRShGd3vbbklVJKqTClLXmllFIKHZNXSimlVDuiLXmllFIKHZNXSimlVDuiLXmllFJKdExeKaWUUu2ItuSVUkp1eDbjXahr0fq0Ja+UUkqFKW3JK6WUUkhYjslrkFdKKaXQ7nqllFJKtSPakldKKaXQJXRKKaWUake0Ja+UUkqJjskrpZRSqh3RlrxSSqkOzybDCb+mvLbklVJKqTClLXmllFIKbckrpZRSqh3RlrxSSimFzq5XSimlVDuiLXmllFKK8ByT1yCvlFJKaTIcpZRSSrUn2pJXSinV4UmY7ievLXmllFIqTGmQV0oppbBj8sG4+VcHeVlEOrfWa9Igr5RSSrUBInIBsMUYUyAiGSIyR0TmisjfRSTKOeZWEZkvIvOc45ukY/JKKaUU4ArhmLyIJAPXABc7D/0FuMoYs0tEbgF+JiJfAVOAE4Eo4GMRmWeMyfVWrrbklVJKqdB7DBgIzBSRsUCJMWaX89wLwDRgKvCKsSqBN4GTmypUW/JKKaUUQV0n30VElnrcf8EY80L9daUv0AuY6Hx9EdhU97wxpkpEIoE0YIVHOVnAgKYurEFeKaWUCq4cY8zEJp6fBLzttM4zRaQI6FH3pDMeXwPkAl09zusBZDd1Ye2uV0op1eHZmfASlJsPtgKn23pIMtAJiBGRns7zvwBmAXOAK53jooHLgblNFawt+Q5kyMBevPDWQ6GuhmrETa8tC3UVlBff3zct1FVQYc4Ys1JENovId0AFcCdQBrwhIi5gLfBrp9t+kYjMc059whiT11TZGuSVUkopwBXChHfGmEeBRxs8PLWR454BnvG1XA3ySimlFOG5C52OySullFJhSlvySimlFLrVrFJKKaXaEW3JK6WU6vAEu91suNGWvFJKKRWmtCWvlFJKEdoldMGiLXmllFIqTGlLXimllPI9BW27oi15pZRSKkxpS14ppZQiPNfJa5BXSinV4QngCsMor931SimlVJjSlrxSSilFeHbXa0teKaWUClNeW/IicoU/BRlj3mx5dZRSSqnQCMcldE111//Cj3IMoEFeKaWUakO8BnljzClHsiJKKaVUqIiE55i8XxPvRCQNOA742hhTIiIuY0xtcKqm2rPKijIKs/dQUVZCTXUVUTFxJCSl0CmtBy5X604FycvaSUxcIgnJqa1arlLh4ED2fg4cyKaoqIjIiAg6JSXTq3cfEhMTQ101dQT4HORF5NfAA0ACMExEKoE5InKdMebbINVPtTMHc/ezffUi8vfvAmMOez4yKobeQ8bQd/h4IqNjWny9yvJS1i/8kuqqSvpnTKT/yEl+jautXzSL/ZkbDnt82KRp9Bw4osX1a09OHdGVNXsOsv9ghd/ndoqNZGJ6Cj2TY4mLjqCgtJKNWcWs3XuwsbdBq4mLcvHOzcdwzjPfBe8i7dT6dWtZuGAeB7L3H/aciDBg4GBOmjKV7j16hKB2bVM4rpP3KciLyHnAb4FxwGoAY8xuEXkA+EhEphpjlgetlqrNM8aQuWYxO9YuafK46qoKdqxbwv4dGxl14tkkdu7SoutuWjqXqopyAIrysv0K8Ln7djQa4DuiAV3ieeySUVTXGJ6etZm3l+zx6bz+afHcPHUgp2V0IzLi8B6aA0UV/HvBDt5ctIvq2taP9rdPH0K/tPhWL7c9q62t5YtPP2b1qpVejzHGsG3rZnZkbmPKqacx8ejJR7CG6kjytSX/W+BBY0ym5z9RY8wbIjIEeBg4Owj1U+3EpiVfs2/bOvf9tF4D6NZ/KAlJKUhEBBWlxeRn7WTv1rXUVFVSXnKQlV+9z/jTLiEhKbBu9v2ZG8nZvRWwPQRDj/ZvGsnBnH10Tx/uvl9ckENJQU5AdWnPXAKPXDSS2KgIiIKBXX3rxj1rdHfuPz+DuOgIr8d07RTDHWcMZfqo7vzmv6vILvK/l8CbyQNTuXRin1YrL1x8NfOLQwJ8//QBjBk7ntS0LrhcQm5uLqt/WMn2bVuoqanhq5lfEBkRybijJoSw1m1D+LXjfQ/yE/A+2/5t4LbWqY5qj3Zv+sEd4COiosk4djppvdIPOSYhKZXUHv3oO3wC6777goLs3VRXVbBm3qccfcYVuCK8B4rGVJSVsHl5/SjR4KNOJCbOvzHGAaOPOeT+9tWLO2SQ/9mJ6YzukwzA7rxSnpm1udlzzhzdnT9ePAqXswH30sx8Ply+l41ZRZRV1tC1UwyTB6Zy2aQ+pCZEM7pPMi9ccxRXv7SUwrKqFtc5MSaChy7IcF9fWRvWr2P5Mtub5nK5OO2Msxg3/tDg3a17D0ZkjGTd2jV8/skMqqurmfXlZ3Tt1o3effqGotoqiHydAVUDeJtgVwH49x9ahY3qygoy1yy2d0QYefyZhwV4T9GxcYw++VyS0uw4YFlRAXu2rPL7upuWzqG60nbTp/ZKp8eAjjV+3loGd0vgxikDAaitNdz34XrKqpqeS9urcywPnG8DbE2t4ZGP1/Ozl5fx0cp9bMwqZmdeGct2FPD3Odu48NmFrNhZAMDArgnceebQVqn3XWcNo2fn2FYpK1zU1tYy75uv3fdPnjrtsADvKWPkKM4693z3uV/Pnhn0OrZ14mw329q3UPI1yH8HHOvluUuBha1THdXe7Nu+jupK2wXbI304qT36NXtOREQkwydPQ8S+/bK2rffrmlnbN5C7Zztgu+mH+dlNr6wIl/DwhSOJjrS/h/8t2c3SzPxmz7v+5AHuLvrn525rcvw+v7SKX76+kgNON/2543rSJyWuRfWeMqwL54/v1aIywtHGDevIy80FoFfvPhw96ZhmzoARGaPIGDUagL17drN508ag1rEtsxvUBOcWSr4G+fuA+0VkGDbxjRGRCGfG/f85z6sOKG/vDvf3fYaO9fm8+KQUkrvZf9QlhblUlJX4dF5FWTFbVrSsm15Z152YzsjeSYDtpn96ZvPd9ACnjugGQFllDa8u2NHM0VBUXs1b3+92358yPPDJlp3jo7jvPO21aczG9fUflicfc5zPLcjjTjjJfezaNf73qqm2zacgb4xZAtwIfIpdQvchkAPcDlxijPk+aDVUbVpR/gEAouMSSEzp6te5njPrK8qKfTpn45I57p6D1J79tZs+QEO6J3LDyQMA201//4zmu+kBkuOiSI6PAmBLdjHlPpwDsH7vQff3vVvQkr/7nOF06dTypZfhpqamhu3btgAQGxfHkGHDmzmjXlpaF/r1Twdg25bNVFW1fM5EuxSkrvr20l2PMWY2MAKYCvwJuBAYZIzRgZwOytTWusfFE5LT/D6/rrseoLa6utnj921bR97eTKCum36q39dUEOkSHrloJFFON/3bS3azZHvz3fRgu/jrRDWyZM6buiEBgKgAkyGdObo700d1B6Ckopqi8ubfMx3Fgez9VFZWAnY2vb+BZeDgIQBUVVWxZ/euVq+fCh2/Mt4ZY6qAb4JUF9XeiHDU6ZcBEBXt/ySo8pL61l10bNNrnctLi9myYp77/qDxJxITr930gbj+5AGM6NkJsN30T83a4vO5eSWVFJZWkRwfxeBuCXRJjCanuLLZ845OT3F/n5nr29CMpy6J0fzh7GHu+098uZnrTkynU6zulg2Qm1u/KqRPADPk+/Xr7/4+J+cA6QMGtkq92pswzIXje0teRGJE5AER2SgixSKyQUTuFZHoYFZQtV0iQlJqd5JSuxOXmOzXubW1NRRk2wlbEZFRxHXq3OTxm5Z8RU2VDSapPft3uGx0rWV4j05cd1I64NFNX1njVxmz12UDEBnh4vbpQ5o9vlfnWC6a0BuAqppa9/n+eOCCDDrH2381323J5d2lviXr6Shyc+qDfEqK/3knUlLre+JyDvj/+1Ftl09BXkTigbnAecDvgcnAPdiZ9V+JiK5lUX7Zn7mRqooyALr0Hthk9+LerWvI27cTsOvwtZs+MJERwiMXZbi72f3ppvf0/DfbOOisdT9nbE/uOGOI1xnEPZJj+NtPx7ln47++cCdZhf4lxLnoqF6cNNTO3ygqr+b+D9c1c0bHU1xc5P6+cwBBPiYmhoQE2zN2sLCw1erV3oTjmLyvfV33A+XAicaYuoGwtSLyMTAbG/DvCUL9VBiqKCth2w/1ucb7DBvn9djy0mK2rlzgvh+flML6xbMoKcyjurKcqJg4ktJ60D19GF37DApqvdu7m6YMZGgP202/J7/Mr256T1mFFdzx9mqeuXwscdERXHVcf47q35lX5u1gaWY+RRXV9EyOZVpGN649ob+7BT57XTZ/nb3Vr2v1TI7ljjPq19Y//vnGgHLrh7uqyvrJcjGxgbW5YmJjKSkpdo/tq/Dga5D/EXCVR4AHwBhTISL3Ay+jQV75oLa2lnXffeluxfcYmEGn1G5ej9+8dI67mx6gKPfQzTYqy0rI2b2VnN1b6dytNyOOnU5MXEJwKt+OjejZiWtOqB933ZVXyj+uHE96l3gSoiPIKa5k+Y4CPlyx16fW/aKteVzzr6U8evFIBndLZFTvZJ748ZhGj62sruWFb7bz4rfb/d6s5uELM0h0xt2/2XiAGSv2+VdAB+EZmKOiogIqIzo62imrY36IqlsnH258DfLdAW8LYrcB3v9LK+Vhy/JvKTxgx1NjE5IYPO4Er8fm7NlOrjObvo4rIpKkLj2Ijomnuqqcgzn7qa6y/5QKsvewYva7jJt6EbEJnYL2GtqbCJfwwAUZh8yGP2bQoasheqfE0TsljnPH9WTmmv3c++G6Zsfqd+WV8t2WXAZ3a3oCZE5xBdsOlPgd4H9yTF8mDbRdz4WlVTw4w7+kSR1JTU19+ysyMrDJiHXnddgldGHK13fDBmA4jQf6kc7zSjVp14YV7N2yGgBxRZBx3Blet5s1xrDth/puenG5SB85iT7DxhERWd9Sqa2tJWv7OrauXODe+Gbtgs8Yf+olfufDD1eXHd3HPZu+zsGyKlbvLqSwrJrUhCjG9u3sHjc/fVR3+qTG8fNXllFc0Xign5DemccuHkWP5Pqu4d15pWw9UEJJRQ1pidGM7JVEYmwkvTrH8eSPx/DVumzu/WCt1zI99U+L57Zpg933H/t0g0+z+DuqiIj6f+XV1dVEBPDer3aCe6A9AeEg1OPnweBrkH8SeEBEZhpT/3lcRGKAR4BHg1E5FT7279jE1pXz3feHTjyFpLTuXo/P2b2V0oO221hcLsacfB4p3Q9fGuRyueg1aBTJXXqy4qv3qa4spygvmz1bVtF32PjWfyHtTHx0BDdOGeC+X1xezZMzN/PB8r3UeGz9GhPp4ifH9uXmUwYRHekio1cSj140il/994fDypw6oiuPXzravfZ9weYc/jp7K+v3FR1yXKRLOGtMD26dNojuSbGcmtGNvqlxXPvysibXuNtd8ep3t5u9NpvPVh++J7qqV9fVDjbIx8T4nzCo0gny0V4+eKv2yevsehE5ru4GbAUKgSUicqmIHC0iVwIrgI3A3iNTXdUe5WXtZMPiWe77/UZMaHYJXJbHPu8Dxx7XaID3lJCcxvDJp7rv71q/HONv/3AYumRib1ISbAAoLK3i6peW8O7SPYcEeICK6lpenreDG/+zgooq29I+ZURXpo44NIvhoK4JPHbxKHeAf/arrdz02srDAjxAda3ho5X7uOzvi1m9287YHtqjE49fOqrJOl97Qjpj+9ollXnFlTzyiXbTNycqur71XVkR2Jh6ebmdJ+P5gaGjkSDdQqmpJXRvNLgNA9KAx7Hbyz4ExGGX070e3Gqq9upgbhZr5n+GqbXpT7v3H8aAMd72OrJqa2sp2G9znUfFxNF7cOMTuhrq0nsgiSl2ekhleSlF+bre95yxPd3f3z9jHZuzm05EszQzn6dm1s+6v+7E9EOev/OsoYcsh3vxm+3N1iG/tIqbX1vJ/kKbHfH4IV2YMqzx/PVDuiVw0yn1iVge/WQDeSU6RtycxMT64ZjCwgK/z6+oqKC0xL43kpL8y3kRLkTAJRKUWyh5DfLGmAF+3DpmeiTVpJLCPFZ98zG11fafdGrP/gybPK3Zca+KkiJqnHNSuvfxa2w9tWf9Lngl+R1vb3hPqQnRDO1uJ8Vt2FfE1+sP+HTe20t2u3eNG9kridQE20rsnRLHsc6EvdziCp6d7fsSvMKyKp79qn75XGO7yEW6hEc8egk+W5XFrAAS53REaWn1H5oKCvzPfZCfl+v+vks3nUcdTgJLIq1UM8pLilg1d4Y7t31Sl56MPP5MXD7kLa+sKHV/72/q2tj4+hZNpbNMr6Ma0j0Bl7MmaP5m3z/wVNcaFm/LA8DlEoY4HxQm9K/PSjhrbbZPG9p4mrUu2z1MMKbv4a3FG08Z6J4geKCogj9+qvN5fZWaVr9aImuf/6OnO3dkur/v2rXjBnmR4NxCyaeJdyLyCnaLWa+MMT9rlRqpdq+yoowf5n7o3lkuITmN0Sedc8is+KZ4Hue5NMgXdT0ADcvpiFIT6sdW/U0gk+V0rQOkOMlsunrs/rbtgP/558sqa8gvqaRLpxg6xx36uxnZK4mfeazj79ophvl/mOJz2asemub+/vHPN/L6wo61yUq37j2Ijo6msrKSnZmZfp+/bZvtZYmKiqJX7z6tXDsVSr625FcCvYGLgYPAMufrxUBX4PApuKpDqq6qZPU3H1FWZMcFYxOSGDPlPL82sPHcH744z7cu5jqe4/AdfQMbz3XusVH+ddrVjbsDlDsT8Sqq/Wu5NybeKddzdn10pItHLx5JpB+72qlDRUREkD7QZnzMz88ja5/vSYPy8/PYmWnnVgwaMrTDL6HrqGltVwK/A8YYY9xr5UXkaWxO+wdavWaq3amtqWHNvE8pyrOBNiomjjFTzj8kaPsiKiaWxJSuFOcf4GBuFsUFOYfsPe9NZXkZuXvsPysRF527dewWyT6P1nhGzyS/zs3oVX98Xa55z9Z9cwlwGjO4WwLxMfZfzu78+qGU8f06s3p3oXsGvq9Oy+jmLm/Givou6q3NTC4MV8OGj2DTBrsSYcnihZx7wUU+nbfou/nulSijRvk2yVW1H74G+QeBuz0DPIAxJlNEHsLuL39aa1cuHInINcArxphQr6xoVaa2lnULv6Qg286Kj4iMYszJ5xHfzO5y3vQcmMHmZXZX401L5jBu6kXNTsDbvGyuu7u+a99BRHXw9b6b9heTV1xJamI0Jw/rQmpClE8z1Qd1TWBMHztmnltcwcb9dnnc4m15VNXUEhXhYlpGN56cuZlSP3awu3xy/TJIzzkCi7fluecA+GNieoo7yN/7gW5aM3zESOZ/O5f8vDzWrV1NxqjRDBrc9C6BWzdvYtXKFQD06duPQUOGNnl8uAv1+Hkw+No/NhFY4OW5+UDTa6JakYhMERHjcasSkYMisk1EvhSR34hI5wbndBWRTBH58kjVs6PZtHQuObvtuJ64Ihh14jlN5qRvTs9BI93bzx7MzWL1vE+o8pJTu7amhg2LZ3Ngl53t7YqManaZXkdgDMxYaVu48TGR3HNO89vzRkYI950/gghnwt4Hy/e609EWlVcza63tpUlNjOb3Zw3zVsxhpgzr4t5utryqRnPQB4HL5eLEk09x3/9kxvts3+Z9Q6Atmzfx0Yz33edOnTY96HVUR56vLfkywFv/XFeg1MtzwXQDcACIAjoBvYBJwB+Be0XkWmPMDOfYeKeeHXu6dZBs++E79m1b674fm9CJrO3rydruexKTTqnd6DN0rPu+yxXBqBPOZvnsd6ipqiQ/ayfff/ofeg0eQ0qPvkTHxlNdWU7Bgb3s2bSKilInGYsIGcdO93t/+3D18rwdnD++F6kJ0Uwb2Y1HLszgoY83UNnI+HpiTAR/vnQ04/vZD1fZByt4eV7mIcc8M2sLJw3tQmJsJBcc1YsIl/DIJxuazHN/8YTe/P6soe4PDi/PyzxkKEG1nhEZo9i5I5OVy5dRXl7OO2+9wZBhw8kYOZqUlBRAyM/PZc2qVWzZvNF93vSzzqFnr8OXNXYkQujXtAeDr0H+C2xQvamR5250nj/SZhpjMhs+KCK9gH8C74nIRcaYj4wxO0SkLxrkW92uDSvYuX7ZIY+VFRW4J975qrqq4pAgD5CQnMq4qRex5ttPqCgrpqqinB1rv2fH2u8bLSMyKoYRx55OWq90v64dzgrLqvjt/1bxwlVHERXp4rzxvZg4IIW3Fu9m2Y58CkurSEuMZvLAVH48qS+piXYmfVllDbe9ufKwPPP7Csv53dur+etPxmq/NyAAACAASURBVBIV4eLccT05YUgaH63cx+JteWQVllNVY0hJiGJMn2TOHdeTYT3qlzXOXLOf531IoKMCd9r0s6iurmbNqh8wxrBpw3r3WH1DERERTJ12OmPGagrocOVrkL8bWCoi5cB9xpgiEUnC5q0/HTg6WBX0lzFmr4hcCHwMvCQig4wxRcD7QCZwDYCI9AaeAaYCFcBLwCPGmEP6hEXkAuBW7JBFBLAWeN4Y87KIfAgUGGPqyqzL5X85kAzMAH5njNknIlOAOR7l1i1JHOD5YUVELgNuBsYD0diUwu8DTxpjCjyOuwZ4BegDvANMAH5qjHknsJ+c/7K2rz8kH30wdErpysQzLmfHuiXs3brWnVjHkysigu7pw0kfOanDz6hvzLLMAm55YyV/uWw0SXFR9Oocx+3TvY/VZhWWc/tbq1i39/BUtQALtuRy039W8Pilo0lNjCYlIZqrj+/P1cf3b/T4Ov/5bgdPzdzi9250yj8ul4uzz72AgYOGsHDBPA5kH573X0QYMHAQJ59yKt269whBLdugNrCmPRh8CvLGmJ0iciJ23/h8EdmP3X52AXCiMWZnEOvoN2NMjYjchA2QPwX+0chh72O7+n+FDZS3A9XYSYaIXffwT+B6bLC+HSgBxgKPi8jpHD6n4SHsh4gHnLJuAd4ETgHWABdiP1Tc6nwPkO1xvX8BlwD/Bv7jlDEK21tytYhMM8ZsbnDN94DN2A8p7/v0A2olPQaMoMeA5sd5WyoqJpbB409k4JjjKDiwh7KiQqory4mMjiEuMZnkrr2JCHB7TU8DRk9mwOjJrVDjtmfR1jwu/tsibp46iLPH9HBnlfNUVF7NO0t28/L8TA6WNZ2f4Pvt+Vzw3EKuPLbfIfnxG6qqqWXB5lxempfJql3+zZ5XLTMiYyQjMkZyIHs/B7KzKSo6SEREBJ2Skunduw+JnXQ75oZCvdwtGHz+z2iM2QgcLyLpQE9gtzGmzWaccLrov8H2NBwS5EUkFTt+P8EYs9x57BUg1+OwO4HrgB8ZY972ePwtEXkKO0QxGpvXv86ZwGPGmL85Zb4KdHbqkwN8WDcp0BjzYYMq34VdoTDeGHPIbBkReRT4CPhARMYaYzz7UEuNMVf68CNp91wREaT26Afa8AjI/oMV3P/hOv702UaOTk+hV+dYEmMjKSitIjOnlJU7C6iu9b2ZXVBqU9U+9/VWhnXvxNAeiXSOjyIqwkVhWRV7C8pYsbOw2X3pA3XmU97mAitPXbt1p2s37zs+qvDmd/PH6VrObPWaBMca4MRGHj+IbZVPBpYDGGOy6p4UkXjgHuAvDQI8zrHZInIp0HCgax8wSUTEWBVAs3tkOte7G7gDyG24OsDxG2Ap9kPL5x6PP9dM2ddjeyPo3qtjrxtXVlllDd9uar28/sbAhqwiNmQ13r2vVHsRjumYvAZ5EdlMM6lsPRlj2uICy3LsTnmHMMZUi8jvgedEZAx2noFnarXjsasJXvRWsDFmq4jMa/Dw/cAsYLaI/NoYs9rHeh7nXO+fzq0pR3NokF/m7UCnni8ALwAMGzVOR0OVUqoDaaol//MjVovg6Q80OqRgjHlORDYBTwKbROQ2Y8xrztN1m2g3N9dgT4MyF4nIBKfMFU5GwLsadK83pm5B+elAc9lKGr6ejr3VmlJKtQKhg43JG2O+OZIVaW0iEocd4/6rt2OMMTNFZCx2aeDLIlLkjJXXJUDvh528500fGnwQMMZs+v/t3Xec1NW9//HXe5ciRRFRlCIiNsTeYkmi2CNqrDGJN4Wb3BjTjCb3JsYYY5KbmF6ummJMrvnF6I2JvXcQxRIQEbAACthAqo26wOf3x/nuMgwzu7Owu7P73feTxzyY+ZbzPfud2T3zORU4SdLRwLWkHvkXNJHd+lqEOdn5zeFhgWZmVlIemyDqXUyaJOfPjR0UEWsi4gpSj/ZPZZsfI7Xbn1PuPEnDgA80ku6DpPn+P11BXseT+gj8WyPXO1jSbdnQxcLrbPqqIWZmRo1a51HVn6m6l295kmolXQxcRJpvf4Pq+uyY4tkf+gDvAUTEctJ4969n49aLz+8P/LPE9uL5AhrSbExELAV+AnxT0lEl0t0F+D9gTUS801R6ZmZmsBG969uR4yTNJzWlbEYaqrYLcCowFLgM+HmZc88G/iTpSuApYCRwOmkMe72fZ+n9XdLZpCFsS4F9gc8BN1PQPi5pR2C8pDtIk9MMAy4kLd5TaFl2/KeBaRExIdv+Q2A4cK+ka4EHSe/P+0g1DFNIQ/rMzKwVVDvqbg0duZD/Q8HzIBXAr5KWvv1jRDxe7sSI+GvWZn8eaaKZ6cBphf0QIq29eI6ku4EvA78i3a/JwBcj4oZsxrv642dJOoY0mc7VpHb9HwI/Lbr8/aRhe38kNSVMyM5fK+kTwJ2kKYRPJ9W0zAQuAX6X1TCYmZlVpOJCXlI/UhX4SFK0/AZpGtW/tE7WSouIMaTovbnnjSx63TC0rInzbiZF7aX2nVr0eizp/jSW3hLSFLSl9gVphrzrKsjXNcA1TR1nZmZNk/LZu76iNvlshrgnSGO5h5Omg90GuEDS7ZI6co2AmZlZLlXa8e57wJMR8XlgLTTMEHcYaQ77b7ZO9szMzNpGZ+5dfwolFnmJiGWkxVgqGSZmZmbWbkmt86imSgv5bSk/+9tUYPuWyY6ZmZm1lErb0l8FBlJ6itidSJ3wzMzMOiQBNdUOu1tBpZH8X0lTv64nWz3tR6S1z83MzKwdqbSQ/xGwnaT/R5qL/UhJ/wm8CLyb7TczM+uwalrpUU0VVddHRJ2kUaSZ3rYHvkFaO/0y4A8VrLJmZmZmbazi8e3ZQih/YP2Z5szMzHIhh03yVa9JMDMzs1ZSUSQvaS1pfviyIqK2RXJkZmbWxiTlsnd9pdX1hePgBXQHhpAWbqkFvtLC+TIzM7NNVGnHu9dLbH4JeFjS34GPAT9ryYyZmZm1pRwG8i3SJv/fwJdaIB0zMzNrQS2xelwt0K8F0jEzM6uaai8m0xo2qZCX1AO4GHioZbJjZmbW9vI6rW2lvevvZ/3e9TVAT2B34GXg5JbPmpmZmW2KSiP5a4terwWWkgr4yRHR6PA6MzOz9i6HgXzFhfyLEfFEq+bEzMzMWlSlhfxDkvaJiBmtmhszM7NqUD473jVnqdkLJXVvzcyYmZlZy6k0kq8FPgycIelJYD6wgoLOeBFxTstnz8zMrG2I/IXylRbyrwJXtGZGzMzMrGWVLeQlXQL8PCKWRcT32jBPZmZmbSqNk692LlpeY23y3wV6t1VGzMzMrGU1Vl0vmlhe1szMLC/yGMk31Sb/upqeHUBARES3lsmSmZmZtYSmCvnDgAVtkREzM7NqqiCo7XCaKuRfiYj5bZITMzOzKumMHe/GAqvaKiNmZmbWssoW8hFxZES81ZaZMTMzqwqlBWpa49GsbEgjJH1G0kBJd0kaK+k6SZtn+8+Q9JikRySd21R6lU5ra2ZmZq1IUl/Sqq9bAJcBF0fEEcDfgIsk9QG+CBwFHAGMlLR7Y2m6kDczMwNqpFZ5VEJSLfAb4NfZpr4R8TRARNwJjAAOBm6OiJXZEu+/A05oLN1Kp7U1MzOzjbO1pAkFr6+KiKuKjvk+8D+kSei2AtYU7V8L9APeLNg2D+jf2IVdyJuZWafXyr3rF0bEgWWvLZ0AnAq8H9iSVNDPLjqsBlgE7FywbTvSgnFlubrezMysiiLi7ojYIyJGAueTFoR7XdJeAJJGAc8BTwKnSOqqNKj/XOCuxtJ2JG9mZkbze8K3sm8DV0nqCbwOnBsR70r6E/Awadr5v0XEC40l4kLezMysnYiIMcCY7OWoEvtvAG6oND0X8mZmZoga2lco3xJcyJuZWacn2l11fYtwxzszM7OcciRvZmamfC5Q40K+E+ndvQsH77RVtbNhJTz5naOrnQUzyyEX8mZmZlDxFLQdidvkzczMcsqRvJmZdXruXW9mZmYdiiN5MzMz3CZvZmZmHYgjeTMzM9wmb2ZmZh2II3kzM+v0RD6jXhfyZmZmAuWwvj6PX1zMzMwMR/JmZmYAOVxN3pG8mZlZbjmSNzOzTk94MhwzMzPrQBzJm5mZ4TZ5MzMz60AcyZuZmeFpbc3MzKwDcSRvHUZEsHDBAubOfZ1lS5dSW1vLFn36MGTIUHr07Fnt7FkTXn5pJi88P41RJ51S7ax0Kgvmv8mCBfN599136VJby+Zb9GHgoMH07t272llrZ5TLGe9cyFuH8Ny0qYx/dCyLFi7cYJ8kdhs+gsNHHkXfrbaqQu6sKStWrOCeu25n6NBh1c5Kp/H8c9N4/LFxLJj/5gb7JLHjsJ05fORRbLvddlXInbUVF/LWrq1du5Y7b7+F56ZOKXtMRPDC89N4+aUZjDr5FHYbPqINc2iVeODeu3nv3XernY1OYe3atdxz5+1MefaZssdEBC+/NIM5s19m5NHHcuBBB7dhDtsnL1BjVgV33X7regX8Hnvtze4j9mTLvn1Zs3oN8+a+wcQJTzH/zXmsWrWKW2/6J6ef+VF23nW3KubaCk1/8QWem1b+S5q1rAfvu2e9An6HoTuy9z77sVW/rampEYsWLWLK5GeY9fJM1qxZw4P33UOX2i7su/8BVcx1++DqerM29Ny0qUyb+iwANTU1nHLamew6fPf1jum/7bbsufc+PHT/vUyc8BQRwZ133Mo5537Z7fTtwLKlS7nv7juqnY1O44Xnn+Ppif8C0u/MsR8axb77rV949992O3YfsQfPTZvK3XfcyurVq7n/3rvYpn9/Bg3evhrZtlaUx9oJy4GI4NFHHm54/cEjjtyggK9XU1PD0cd9iKHDdgJgxfLlTJzwVJvk0xp37913smzZsmpno1NYu3Yt48Y+1PD6iKOO2aCALzRijz0ZdfIpDec+9MB9rZ7H9k6t9KgmF/LWLs2e9TJLFi8GoG/frTjo4EMbPV4Shxz6/obXM6e/2Kr5s6ZNm/IsM6a/UO1sdBovvvAcixctAmDgoMEc9L5Dmjxn9xF7MmLPvQB44/XXmOHfm9xxIW/t0muvvkKPHj3o0aMH7zv0MGpra5s8Z8DAQQ3Pl7y1pDWzZ0149913eOD+e6qdjU7lxeefb3h+8CGHVdy+fNgHDm84tr55rFNSChZa41FNbpO3dumDRxzJB484slnnREQr5caa6547b2flihUA9N58c/esb2Vr1qxh1sszAdisRw922W14xef267c1Q3YYypzZs3h55gzq6uro2rVra2XV2pgjecuN1159peH5llv2rWJOOrdJT09g1ssvAbDZZpsx8qhjq5yj/Fsw/01WrVoFpN70zY0eh+28CwB1dXW8/tqrLZ6/jqB+CF1rPKqp2tc3axGrV69m3Jh1nY52bUYkYy3nrbeWMOahBxpeH3P8CZ5ZrQ0sWrRukqjBG9FDfsiQHRqeL1y4oEXyZO2DC3nr8JYuXco/b7ieN9+cB0CPnj3Zb/8Dq5yrziciuOv2W6nLIspddh3OiD32qnKuOofCmSD79m3+rI99t+rX8HzhgvktkqeOyG3yZu3A2rVrWbliBQsWzGfmjOk8O3lSQ/tvbW0tp51xFj179apyLjufCU890dBk0qNHT4474cQq56jzeO+9dX0ettyIQr579+706tWbpUvf4523327JrFmVuZC3DueWG28oOdRnyy378uHTzmTAwIFVyFXntmjhAsaNXTevwXEfGkUvf9FqM3Wr6hqed99ss41Ko/tmm7F06XsNbfudUbXHtLcGV9dbbtTU1rBkyaJqZ6PTSesLpJnTAIaP2IPddvf6AW2psGDe2J7x3bp1y9Ja2SJ5svbBkbx1ONsP2YEIWLZsKQsXzG/4A7d40SJuv+UmXnhuGiefeoaHAbWRJ8Y/yry5bwDQq1dvjj3uhCrnqPNZs2Z1w/MuXTbuz3r9eXV1dU0cmV85nLrehbx1PAcdfGjDDHgRweuvvcozkyYybUqayGPG9Be58YbrOevjn6CmxpVVrenNN+cx/tFHGl4fd8KJXjOgCmpr1/0pX716dUWTRxVbnRXunfXLcRpCl79S3n8BrUOTxODth3DSh0/j7E+OZrOsPXLO7Fk8+fhjVc5dvq1Zs4a7bruFtWvXArDnXvuwi1f/q4r6qnagodmkuVZlhXy3bt1bJE/WPriQt9zYfsgOnP6RjzUMWXli/KOduhNRa3v0kYdZkA232nzzLTjq2OOrnKPOq2u3ddH3qpUb16a+YsVyYP0vDJ2N1DqPanIhb7my/ZAdGqb0XLVqFTNnTK9yjvLpjddf46knHm94/aETT2qoRbG217v35g3P3377rWafv3LlSpYtXQrAFlv0abF8WfW5kLfc2XnnXRuevzlvbhVzkk91dXXcefutDWsF7L3v/uw4bOcq56pz69dv64bnb23E4kxLFq8blbJ1//4tkqeOR632r5rc8c5yp/fm66KalStXVDEn+TT24QfXKxSefeZpnn3m6YrOnTplMlOnTG54fd7XvuEagBawVb91M9bNm/sGNLKOfCmvzJnd8HybbTprIZ9PLuSt3Xlu2pSGBU4OPvT9bL31Ns06v75tEaBnD/f0bklzZs/i6QlPVTsbVqT/ttvRrVs3Vq1axSuzZzf7/Jez37euXbsycNDgFs5dx1Ht9vPW4ELe2iEx9dkU7fXq1avZq5gVrkbXr5lfEKxxr74yhz332qdZ5yxd+l7Dl7Yt+/Zl8OAhDfs2ZqiXbai2tpahw3Zi+gvPs2TJYubNnct2AwZUdO6SJYt5ZfYsAHbaZddOO4Qur1zIW7szbNhOdOnShdWrVzP5mUkc+v7D6d69smE9y5ct47mpUwCoqalpWELTWsYHDh/Z7HNemTO7oZAfPHgIo04+pYVzZQC7Dd+d6S88D8C/nnyck089vaLznhj/aEP/ij333LvV8tfeeZx8ByNppKSQNLTM/qHZ/pFtmjFr0mY9erBH9sdmxfLljHnw/orOq59edUW2WM3e++5Hjx49Wi2fZu3J8N33oO9WaXGa56ZN4aWZM5o856UZ03n2mUkADN5+CDvtsmsTZ1hHk9tC3jq2Dxw+smGhjWcmTeSRMQ81TLpSyrKlS7nxhut5aWYaMte79+Z88PAj2ySvZu1BTU0NHzxi3Wf+jltvaqhBKWXmjOncdutNDecedUwnn+eglcbIV7ud39X11i713nxzRp34YW656R9EBI8/No4Z019gz733ZeDAQfTq1Zs1a9fwzttvM+vlmUyZ/EzDxDfdunXjzI+e7eVmrdPZfcSevDJnNs88PZEVK1bwj//7G7vsNpwRe+xF3759AbFkySKmPvssM2esW8nx+FEnefVGql8gtwYX8hlJlwKjI2KopFHAhcB+wHvATcC3I+KtguO7A/8NfBzoA9wK/FdEzM32jwFmR8ToMtebDVwTEZdmrwP4d2A48BXggYg4peD4XYGLgGOBbYCFwFjg5xExsSXuQXuz6/DdOeX0M7nrjttYtXIlCxcsaLLqfqt+/TjtzI82u0e+WV4ce/woVq9ezdRnJxMRTH/h+Ya2+mK1tbUcdcxx7L3Pfm2cS2srLuSLSPoGqYC/Evg9sBtwAXCIpIMjon5i6O8Do4FLgdXAl4DrgE2pIz4X2Ao4D3iyIE+jgBuA8cCPgQXAIOAjwJOSzouI327Cddut3YaPYMCAQYx/9BGmTX227LzcW/Tpw4HvO4T99j9wo1fhMsuDmpoaTjz5VIbttAuPPzaOBfPf3OAYSew4bCeOOPJo+m+7XRVy2T5Ve+Ka1uC/husbSIqi94uIOfUbJd1DKmA/AlyfbT4BuCwirsyOuQbYchOvvx+wR0TMLLj2zqQC/usR8Yei438h6XzgCknPRMT4Tbx+u7RFnz586MSTOfq4DzH3jddZtHAhK1Ysp7ZLF3r17MW2AwY4cm/HhuwwlG9cdEm1s9Hp7D5iD3YfsQcL5r/Jgvnzeffdd6itrWXzLfowaNDg9SaNsvxyIb++rsD5hQU8QEQ8LmkCcATrCvm5wPskKZKVwIZfmZvn3sICPnMRMBH4u6RSXyKuAc4Cvkr6IpJbXbt2ZcgOQxmyw9BqZ8Wsw9im/7Zs03/bamej3RNQk79A3oV8kTrgtjL7ZgGFs0t8F7gfeEDS+RExpZnXKjWyoVTb+nGkqvmmJqQuWecm6RzgHIDthwwpdYiZmeVUngv5+vFW5b6b1U+1VVewbXFE1JU6GFgONKwCERFPSDoA+CUwSdKvgW9GxJqi65dTanaXhSW29c+ucXsT6ZXMd0RcBVwFcMABB0YTaZiZdVpuk+9Y6hdVLjdVWu/s/7dLnFNKUBR9R8R04CRJRwPXkr44XJDtXgqUnIlFUjegX4ldy0tsWwCsjIgxjeTNzMxsA3meDKd+jdFy85ruQ+oVP7tg20ZFuhHxIPBfwKcLNs8DhpU55RTW1SQUKhX93wecJankFzJJXSXdIumoZmTZzMyK5HEynNwW8hHxCjAFuFjSehG1pG2BbwMPR8R7G5O+pIOKNvUhjamvNw44UNKhRecNJVW/l2sWKHYZqdf/lZLWe7+ygv8q0rC9puewNDOzTiXP1fUAnwEeACZL+iMpah8KfI0USZ+6MYlK2hEYL+kO4B+kiP1C0hj2etcD5wP3SPoV8CIwgjSe/ifAFyq5VkRMl3R2lt7+kv5CqsIfRqo5GAScERGvbszPYmZmidvkO5iImCBpP+BbwJdJPdAXAHcBP4iI2RuZ7ixJxwDfA64G5gM/BH5acExdVoX+I9IkN72BScAnIuIuSRUV8llat0jaP/s5LiK1588lfYH5adY3wMzMbD25LuQhFchkQ8iaOO5S0ux15faPLno9FhjZRJpvAV/MHsX7hha9bvQrZEQ8D3yqsWPMzGzjeJy8mZlZbimX1fW57XhnZmbW2TmSNzMzawfD3VqDI3kzM7OcciRvZmZG+TnQOzJH8mZmZjnlSN7MzDq9NIQuf7G8I3kzM7OcciRvZmaG2+TNzMysA3Ekb2ZmBrkM5R3Jm5mZ5ZQjeTMzM7zUrJmZWW7lcASdq+vNzMzyypG8mZkZuex350jezMwsrxzJm5mZQS5DeUfyZmZmOeVI3szMOj2RzyF0juTNzMxyypG8mZmZPE7ezMzMOhBH8mZmZuSyc70LeTMzMyCXpbyr683MzKpM0i8kjZE0XtIJkkZIejjb9ltJXbPjviLpUUnjJJ3aVLqO5M3MzFDVhtBlhfXiiBgpqSdwP/A28KmIeFXSl4DPSHoQGAl8EOgK3C5pXEQsKpe2C3kzM7PqmgGMA4iIZZLmA6sj4tVs/1XAdUAA/xsRAaySdB1wBHBTuYRdyJuZmdGqQ+i2ljSh4PVVEXFV/YuImLYuD/ow8BQwuGB/naQuQD9gUkE684AdG7uwC3kzM7PWtTAiDmzqIElHkqrivw38tWB7V2ANsAjYpuCU7YD5jaXpjndmZtbpqRUfFV1fOhw4GfhGRKwCekkakO3+HKmd/mHgk9nx3YCPA2MaS9eRvJmZWRVJ+iBwO/AM8LBSu8FXgL9JqgGmAedn1fZPSBqXnfqLiFjcWNou5M3MzKBq4+QjYhzQp8Suo0oc+xvgN5Wm7ep6MzOznHIkb2ZmhpeaNTMzsw7EkbyZmRn5XGrWhbyZmRm5XJ/G1fVmZmZ55UjezMysOTPXdCCO5M3MzHLKkbyZmRkeQmdmZmYdiCP5TuTppycu7NFVc6qdjxa0NbCw2pmwDfh9ab/y9t7s0FIJCQ+hsw4uIrZp+qiOQ9KESpZvtLbl96X98nvT+biQNzMzI5ed690mb2ZmlleO5K0ju6raGbCS/L60X35vGpPDUN6RvHVYEeE/WO2Q35f2y+9N5+NI3szMjHyOk3chb2ZmRj6H0Lm63qpG0khJIWlotfPSGrKfbXS189ERSBotKaqdD7O8cSFvDSRdKml2tfNhzVfwhan+USfpHUkvS7pX0gWStiw6ZxtJsyXdW618d1ZNfcGVNDTbP7JNM9bJqZUe1eTqerN8+TywAOgKbA4MBN4H/Aj4jqR/j4hbs2N7AtsAy6uRUTNrfS7kzfLlvoiYXbxR0kDg98CNkk6PiNsiYo6k7XEhb5ZUO+xuBa6ut5IkXSNpjKQjJM3Kqn6HlNtecF4PSd+UNFnScklvS3pU0n9IqujzJqlW0uclPSnpvewxQdJ/Stqs6Nhxkm5vJK2/S/pXie2nSnowy9972bU+k+27RdI1Bcc2tBdLOkTS3ZLekrRQ0rWSBhelPbugffl/s2rXSyv52VtLRLwBnAbcB1wtafNs103A7+qPkzRI0j8lLZY0V9IPJHUvTq+Z96+7pJ9Jek3Su9k9G5DtG5ndq//NXtc3Nwwtut5Z2efu7exzNVXS90s0QYzOzh8kabyklZI+ssk3sMoKm9IkjZL0SHYv50q6ssR9KHvPs/1jCt+jEtebXfiZze7paEk/lrRU0q1Fx++a/W14XdIqSW9Iul7SAS10C2wjOZK3xmwH/BO4HJgcEa8odT/dYDuApH7AvaRFMK4BZgC9gMOAK4CPSzopIspGjlkhfguwP+kP/xWkqucDgEuAT0o6JiIWZKf8khSd7hIRM4rSGgycDnyqYJtIEe05wK3A14ClwD7ATyUdR5kvv5LOzs79E/D/gKHAecBjkvaOiLezQ88hVYXfnN2jh4AXyv3MbSUi1kj6AvAS8AkKCvcCN5Hu91eBwaT7sxr4Hmz0/fs+MBq4NEvrS8B1wJHAVNKXj6OAr2TPAeYXXO9PwJnAX0j3fTWwJ3Au8Ons87Deew/cSPr8XZ39TLkg6RvAhcCVpPdhN+AC4BBJB0fE6uzQxu75xjoX2Ir0mX+yIE+jgBuA8cCPSc1Fg4CPAE9KOi8ifrsJ120Tqf08h6F8RPjhBxEB6Q/C7Oz5NUAAlxQdU3J7tu8e4EGgZ4l9ewCvA78v2DYyS2towbbfA88C25RIYxCp/f4wngAADv5JREFUULinYFsNMBO4vMTxPwFeA7oUbPsm6Y/eWSWO7w88DdQB1xRsH53lcwGwX9E5Q4GVwLdKpBfA6DZ67za4l40c+yBwc/Z8TP3PSvoDHsD+BcduB3TdxPv3LPC1gtfdgW2Lzh2d/hxtkOaFwKvATiX29QHGZp+J2qL36qG2/N1p6fcr+1wFMDJ7fSmwKrsXOxQde2h27McrveeF73uZ688GLi36LK8Edi46bmfgPeDzZdI5H1gLHFbte97UY8+994vp85a1ygOYUK2fy9X11pi1QKlv4Btsl3Q4cAwpsusmacvCB6mAvwz4rKQtSl0sq6L9HKkgqSuRxlLgIuB4SbsDRMRa4DfAaEl9CtLqmaV1eWTRTbbtYuDnEXFD8fUjYj4p+ig3lOvHETGp6JzZwG3AEWXOaY+mUnqJzndI9/jg+g0RMS8i6mCT7t9c4H1ZVE5ErIyIN5vKZHa9bwP/DSwq8XkQKYodARxXdPoVTaXfAXUFzo+I9ZaLjojHgQms/xncqHvehHsjYmbRtouAicDfi9+f7D26BniCVDPUvimNk2+NRzW5kLfGvBIRpdaeLrX9OKAWeAZYUuZxOamJaN8y1zuG9Jm8q5E06tsCDyo478+k6PGzBds+TYpeCqfxfD/QG/hjmesTES8B48rs/meZ7bOAAWX2tUcrgB7FG7MvQxcCV0j6naTipYk39v59FzgReEDSXs3I52HZ9X5P+c/DRFJhf1DRuRObcZ2Ooo70hbKU4s/gxt7zeqXKhlL39DjgcMq/P0tINQ3F74+1EbfJW2NKFfDltvcHniO1/TVlWpnt/YHFwBkVpNHQBhsRSyVdBXxZ0q9JkeR5pOrIJQXn1BdarzSR9utltpeLhJaThqt1FDuQqn03EBFXSJpO6uswPWtP/Wu2e6PuX0Q8kXXA+iUwKXuPvhkRa5pIp3/2/3GkAq4xxT9Puc9ue7E2+79cnFeb/V/4cy+ur1UpYTmpLwxQ0T1fWyKNQht0tqT87/0vgbKdXzNNvX/tQg5b5F3IW6PKdZArtX0B0DcixmzC9RYAWwD/ioilzTz3clJTwSmktsPdsueF5mf/DyF1PitnMKULspVljg86SK2YpB7AscD/lDsmIu6TtA/wBeDPkt6NiFvYhPsXEdOBkyQdDVxLKsQuaCK79Z0r52TnN0d7HxZY/1kqVZhCqsEAeLtgW7nPH5T4DDZxz5dSojYHQFI3oF+JXeV+71du4u+9taIO8YfJqqbct/1S2+8DBkgq23tX0lcl/biR6z1A+kye1UgaZ0j6i6Tawu0R8Tqph+/52ePOEgXDY6R253MaSX8Y8IFS+yLrSdTBXUyqdfhzYwdFxJqIuILUo71+dMIm3b8s3QeB/yI1pzRlPKkw+rdGrnewpNuK+3lkfTXas7nZ/7uU2b8PqYPj7IJtG/X5K3PP5wHDypxyCutqEgqV+70/S1LJgFFSV6UhlUc1I8vVk8Mp71zIW4uIiLGkXttXK02wsh5JJ5OG18wt3leQxixSR52fl2pHlHQwqY19cZmq3l+Q2gePBX5VIv3lpE5cX5e0wRcJSf0p3+7eoSnNPXAxqaPUtyNig+r67Jj9ijb3IfWe3uj7J6m4PbYhzcZktTk/Ab5ZqpCQtAvwf8CaiHinqfTak0jDTqcAF2e1Kw0kbUvqcPhwRDR5n0qp4J6PAw6UdGjReUNJ1e+VVq9fRppV8UoVzYORFfxXkYbtFQ9xbIfUav+qydX11pLOBu4HJkv6A6kTXl/geODDpF/4y5tI4yuk4UNPSroaeJw01v5w4KOkYXrfKnViREySNJbUbPBQmfR/Toqe/p6Ne7+NFC3uS+qNfzNl2qs3wnJgpKS3gNvaKLo8TtJ8UvywGbAl6ec9lXRfLyPdg1LOBv4k6UrgKdIwr9NJY9jrNev+SdoRGC/pDuAfpOjxQtIXvkLLsuM/DUyLiAnZ9h8Cw4F7JV1L+iLZhTRV76dIBeVn6Zg+Q6q9mizpj6SofSip2amW9J41W4X3/HpSjdc9kn4FvEgapfAl0herL1RyrYiYnn0Orgf2l/QXUhX+MFLNwSDgjFJfKq1tuJC3FhMR87PI4HzgY9n/S0l/iM+KiBsrSGOZpONJVcKjSX/A60iTyXyR1JmusQ5bv6B0e2J9+gGcI+lu4MukiL8LMBn4YkTcIOmWpvJZoV+T7sGHSePNV7VQuo35Q8HzIN3/V0njov+YDbcqKSL+mkWV55EmPpkOnJbV0tQf06z7FxGzJB1DmkznalK7/g+BnxZd/n7SGPs/kpoSJmTnr5X0CeBO0rz8p7NuboRLgN9FI5MrtWcRMSGrOfkW6V5uRyog7wJ+ECWmJ64w3SbveUTUZbUjPyK9172BScAnIuKubNKkSq93i6T9s5/jItLv31zSF5ifbkR/iqqp9nC31qB8NDOamZltvL32PSBue+CxVkl72DY9JkbEga2SeBMcyZuZWafXDvrItQp3vDMzM8spR/JmZmaQy1DekbyZmVlOOZI3MzMjn0vNOpI3MzPLKRfyZm1E0jWSInuslvSOpLGSPtiG1784e36SpIWSSi0521Q6l2YTFbV0/oZKWt3I/jHZmPnmpjta0gOblrsN0hwpqXjZVevgvNSsmW2qn5CWBB1CmlFuAmnWsT3bOB9LSBPKLGvj65q1Wzmcut5t8mZt7L2ImJc9fwN4OpstbDTwn22ViYh4DDikra5nZtXhSN6s+urIfhezKunzJD0mab6k3tn2bpK+K+klSaskvSrpCkkbTOEraV9Jd2TNAW9LulXSbkXHlKwal3SApJslLZK0XNKzkr5ev8qYpAC+C3w2a3Y4s+DcgyTdk113qaRxkoqX+0VSF0kXSZohaaWk6ZIuYCP+Hkn6rKTJklZIek3SzyT1lPRWtthK4bEDlFYwXChpWXavjyiRZsX32nKklarqXV1v1kllhdHngWOAWwt2XQL8DTgwIt5TWlb3dmAn4JOkBWLOBrYCnpK0TUGaI4FHSPOQHwq8nzRv/QPAegV9ifycDIwlzVl/ImnBkotJCwPdkeVjAGl9gOuy57dn5x4D/J20KtyBwAHAX4HfSvpawTVqSYvYfAT4apanLwInAVdWct8K0roc+A5p4ZXhwMezPN3LhkulDiDdl5mkVQr3yfJ+u6SPFOWvontt1hG4ut6sbX1H0oWkproewMvApwsXgQEmR8RvC15/DlgQEZ8q2DYHGCfpKtLCIF+T1JW0/vt3IuI3BcdOlfQ08DBpoZcNSNqStMzvmRFxT8GuWZLuBU4D1kbEPEnvAcvrmx0kdSMtjHNiRDxfcO4Lku4HnpH014hYAPwHqYDds2B52NmSHiR9EamIpONITRx7FSzkMju7J5ez4Zr2I7KfrXCRpF9ImkNaee+BiFhChfe60nxaR1PtFvSW50LerG1dmT3qgMVl1gsvXib3Y8AhkkotPVpLWqEPUtTekxLL+UbEWEmPNJKvk4Dnigr4+nNXkiL0cg4DdgT+pdJ1kz2yY24lRds/K17/PSJC0g+ADarPyzgbuLrMSm2XkFZ1K/RiqVUQI+Kfki4BRpFqTyq912Ydggt5s7a1OCJeauKY+UWvBwJfIUXipdQVHDezkXXrX2zkmkNItQobYyAwjRTtl/NmwbHllh5tLH+lrlly2dyIWCJpUdHmWY2kNZ207nl9upXca8sZUf3289bgQt6s/VlR9Hou0DciNhiXLWkAaQ1ySL31d5FUU6ag34G0tnwpc4ENOslVaC7pS8KciNigEJS0fUS8W5DH4aR281L5q9TrlOljIKkvaU3zQo31RxgO3JQ9r/Rem3UI7nhn1v5E0esbgK/W97SvJ6kncA+pgxjAY6Rx718vTlDSYaQOZ+XcAYyQtEE0LmkzSR9Vmbr47LrvsmEVOZL+DRhT3zsfuB74r6wPQOFxNaRq9kpdC/xHcQ/6zPdLbNtR0r+XyN/ZpCj+rmxTpffacsjj5M2sGv5Aqgp/UNK3gJeAPYDvkSLavwBERJ2k0aQe431Jvdu7kHrKn0uZ6u3s3AVZT/+/SfoZ8A/gPWBfUg/2ucCNwOrssZWkbSPizYhYJemzwI2SepAKyu6kHvRfA06JiPrheleTagweyX6WaaQo+2JgYaU3JCIelHQN8ESWzmNAf+AL2f/FfR0mkb5cDCNF7Uuze3oxMDoi3sqOq+hem3UUjuTN2rmsgDyRFEleTWpD/h/SUK9TC6vmI+Ih4HBSD/YngUeB/Ukd2hqdhjUirgOOJ02SM55UAH8b+BPrF9QPZOlNKjj3XtJQwPrtjwEHA0dHxMMFx60hFfI3AL8htcP/mhSZX9DM+3IeqZA+H5hCGtY3k3Sv1hQdvjjL26As/5Oyn3VUYYe85txry588jpNXRHHNoJmZWeeyz34HxD1jylZ2bZKBW3afGBEHtkriTXB1vZmZGeRyqVkX8mZmZlD9XnKtwG3yZmZmOeVI3szMjFwG8o7kzczM8sqRvJmZdXrtYbhba3Akb2ZmllOO5M3MzMjnEDpH8mZmZjnlSN7MzAxy2b3ekbyZmVlOOZI3MzMjl4G8I3kzM7O8ciRvZmZGPsfJu5A3MzNDHkJnZmZmHYcjeTMz6/REPqvrHcmbmZnllAt5MzOznHIhb2ZmllNukzczM8Nt8mZmZtaBOJI3MzPDS82amZlZB+JI3szMTPlsk3chb2ZmnZ7wKnRmZmbWgTiSNzMzg1yG8o7kzczMcsqRvJmZGR5CZ2ZmZh2II3kzMzPyOYTOkbyZmVlOOZI3MzMjl53rHcmbmZnllSN5MzMzyGUo70jezMwsp1zIm5mZkcbJt8a/iq4t/UjSI5LGSjqspX4mV9ebmVmnJ6o3hE7S0UDPiDhcUh/gZknHR0TdpqbtSN7MzKy6jgauBoiIt4GxwF4tkbAjeTMz6/SefnrivT26autWSn4zSRMKXl8VEVcVvO4HvFnweh7QvyUu7ELezMw6vYj4UBUvvwjYBliQvd4O+FdLJOzqejMzs+p6CBgNkLXJHw5MaYmEHcmbmZlVUUQ8IOkYSWOzTd9qiU53AIqIlkjHzMzM2hlX15uZmeWUC3kzM7OcciFvZmaWUy7kzczMcsqFvJmZWU65kDczM8spF/JmZmY59f8BlJpxMGpQDoMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TFIDF confusion matrix\n",
      "[[974 249   1]\n",
      " [261 684   0]\n",
      " [  3   4   0]]\n",
      "BoW confusion matrix\n",
      "[[970 251   3]\n",
      " [274 670   1]\n",
      " [  3   4   0]]\n"
     ]
    }
   ],
   "source": [
    "cm2 = confusion_matrix(y_test, y_predicted_tfidf)\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plot = plot_confusion_matrix(cm2, classes=['Irrelevant','Disaster','Unsure'], normalize=False, title='Confusion matrix')\n",
    "plt.show()\n",
    "print(\"TFIDF confusion matrix\")\n",
    "print(cm2)\n",
    "print(\"BoW confusion matrix\")\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 词语的解释\n",
    "importance_tfidf = get_most_important_features(tfidf_vectorizer, clf_tfidf, 10)\n",
    "\n",
    "# 画图\n",
    "top_scores = [a[0] for a in importance_tfidf[1]['tops']]\n",
    "top_words = [a[1] for a in importance_tfidf[1]['tops']]\n",
    "bottom_scores = [a[0] for a in importance_tfidf[1]['bottom']]\n",
    "bottom_words = [a[1] for a in importance_tfidf[1]['bottom']]\n",
    "\n",
    "plot_important_words(top_scores, top_words, bottom_scores, bottom_words, \"Most important words for relevance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4、word2vec\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "\n",
    "word2vec_path = \"./data/IV/GoogleNews-vectors-negative300.bin\"\n",
    "word2vec = gensim.models.KeyedVectors.load_word2vec_format(word2vec_path, binary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_average_word2vec(tokens_list, vector, generate_missing=False, k=300):\n",
    "    if len(tokens_list)<1:\n",
    "        return np.zeros(k)\n",
    "    if generate_missing:\n",
    "        vectorized = [vector[word] if word in vector else np.random.rand(k) for word in tokens_list]\n",
    "    else:\n",
    "        vectorized = [vector[word] if word in vector else np.zeros(k) for word in tokens_list]\n",
    "    length = len(vectorized)\n",
    "    summed = np.sum(vectorized, axis=0)\n",
    "    averaged = np.divide(summed, length)\n",
    "    return averaged\n",
    "\n",
    "def get_word2vec_embeddings(vectors, clean_questions, generate_missing=False):\n",
    "    embeddings = clean_questions['tokens'].apply(lambda x: get_average_word2vec(x, vectors, \n",
    "                                                                                generate_missing=generate_missing))\n",
    "    return list(embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = get_word2vec_embeddings(word2vec, clean_questions)\n",
    "X_train_word2vec, X_test_word2vec, y_train_word2vec, y_test_word2vec = train_test_split(embeddings, list_labels, \n",
    "                                                                                        test_size=0.2, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        0.00375076, -0.15124639,  0.02915809, -0.01554943, -0.10182699,\n",
       "        0.05523972,  0.00953747,  0.0834525 ,  0.00200544, -0.0238909 ,\n",
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       "        0.05023847, -0.09430913,  0.20582217, -0.05274091,  0.00881231,\n",
       "        0.04394059, -0.01748512, -0.0403268 ,  0.03178769,  0.06038993,\n",
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       "       -0.00288318,  0.07032367, -0.07524182, -0.01155599, -0.0259661 ,\n",
       "        0.00625901, -0.05474758, -0.00059877, -0.01737177,  0.07586161,\n",
       "        0.0273136 , -0.00077093,  0.0752638 ,  0.05861119, -0.15668742,\n",
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       "        0.08524286, -0.10112653, -0.14161319, -0.05427769, -0.01017171,\n",
       "        0.09955125,  0.02694847, -0.0915055 ,  0.09549531, -0.0138172 ,\n",
       "        0.01547096,  0.00868443, -0.04557078, -0.00442069,  0.01043919,\n",
       "       -0.00775728,  0.02804129,  0.10577102,  0.07417879, -0.0414545 ,\n",
       "       -0.10446894,  0.07996532, -0.06722441,  0.0636742 , -0.05054583,\n",
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       "       -0.02797735, -0.06948853,  0.06283715,  0.10689872,  0.02087112,\n",
       "        0.05185082,  0.06266276,  0.01831927,  0.10564604,  0.00259254,\n",
       "        0.08089193, -0.01426479,  0.00684974, -0.03707304, -0.1198062 ,\n",
       "       -0.05715216,  0.01687549,  0.03455462, -0.08835565,  0.05120559,\n",
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       "       -0.03065236, -0.04041981, -0.01071312, -0.05153402, -0.14723714,\n",
       "       -0.00877744,  0.08035714,  0.00351824, -0.10722714, -0.03078206,\n",
       "       -0.00496383, -0.01665388,  0.0004069 , -0.02276175,  0.14360192,\n",
       "       -0.09488932,  0.00554548,  0.13301958, -0.02263096, -0.03730701,\n",
       "        0.03650629, -0.02395339,  0.00687372, -0.02563804,  0.03732518,\n",
       "       -0.02720424, -0.0106114 , -0.05050805,  0.00444685, -0.02968924,\n",
       "        0.07124983, -0.00694057,  0.00107829, -0.08331589, -0.03359186,\n",
       "        0.0081293 , -0.0008138 ,  0.01801554,  0.02518827, -0.03804089,\n",
       "        0.06714594,  0.00194731,  0.08901033,  0.06102903,  0.03237479,\n",
       "       -0.05186026,  0.02203078, -0.02689325, -0.01497105, -0.07096935,\n",
       "        0.00406174,  0.03199695, -0.05650693, -0.00124395,  0.08180745,\n",
       "        0.10938081,  0.0316787 ,  0.01944987, -0.02388909,  0.00355748,\n",
       "        0.0249256 ,  0.00739524,  0.0506243 , -0.01226516,  0.01143035,\n",
       "       -0.09211658, -0.02129836, -0.11622447, -0.04239509, -0.05391511,\n",
       "       -0.00467064, -0.01021031,  0.00030227,  0.12456985, -0.0130964 ,\n",
       "        0.02393832, -0.04647537,  0.06130255,  0.02752686,  0.04820469,\n",
       "       -0.06352307,  0.0357637 , -0.1455921 ,  0.01995268, -0.04385739,\n",
       "       -0.03136626, -0.04338237, -0.08235096,  0.02723331, -0.01401483])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_word2vec[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "fig = plt.figure(figsize=(8, 8))          \n",
    "plot_LSA(embeddings, list_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型\n",
    "clf_w2v = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', \n",
    "                         multi_class='multinomial', random_state=40)\n",
    "clf_w2v.fit(X_train_word2vec, y_train_word2vec)\n",
    "y_predicted_word2vec = clf_w2v.predict(X_test_word2vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.777, precision = 0.776, recall = 0.777, f1 = 0.777\n"
     ]
    }
   ],
   "source": [
    "# 评测\n",
    "accuracy_word2vec, precision_word2vec, recall_word2vec, f1_word2vec = get_metrics(y_test_word2vec, y_predicted_word2vec)\n",
    "print(\"accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy_word2vec, precision_word2vec, \n",
    "                                                                       recall_word2vec, f1_word2vec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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75ihNP4ybpXAztQsnuZO64Y3f8J7+VETGNMP5TTOyBWpM1KnqbhE5HVcivQXXHvglEdmKG7O8G9dLuxOuWvosanuT78BNsfpqkLRneSWqF3FTd14tIu/hqqorcYvTTKa249qfqG2LjLZHcTPmnQrMFZG3cf0MEoBTcJ9DCa4kOIqjRynk4AJyV2CiiLyO6zl+BjDB2+eHYYx7z/Wq4GfgSnbLRWQ2LqAXeuc5i9pJYmYBX1LVY9W5rqV4ADc6+nu4APotEXkHN5tiAq7z43m47/Bs3EXu0sBJ+dR0XDxLRH6G67D4tDeBU8S8/7v7cX1jbvDmCziCqk4VkX/ggv0UETkp0GyAsSDape7mYEHetAjeRBy3ishjuCrzi3EBPVhb83rcOvKPNfSDo6ozROQk4FfAlcAV3s3fCuBHqtqc8+iHRVVLReQc3CI4X8W1vV/it8unuOl1V3ml+rrH7xA3D/lTuKDyTb+XS3ABPpyJglDV9V5p7kfAXbiLiPF1dsvDNY88EetToQbizTh3n4h8iPvOjcT9/fztw7WB/1FVq0IILu96x3Sjdg75Nwl9uN9RxM2AWLMA0K/06BUT/d2LuzAciKupuDPS87ZYLWDimuYgNs+BaYm8Tj6jcavEdcSVeg4D24DFqrq5nsPrS7cDrrTZAzc7WS4wr6VP2+m1cZ+J+5HfC8xX1VCr2RGRwbhg3AHYCbyjqnmNzFOKl+YA3Ep0B3AXS59rwyufHTfErT9/Gq62qBh3gfpRuDUcItIVNyqiPbALeFVjednXYyy+Qx9tM/mRZkm74KUbF6rqUU1ux4IFeWOMMce9hA59tc35zRPk81+8IWpB3jreGWOMMTHK2uSNMcYYYrPjnZXkjTHGmBhlQd4YY4whepPheHN1/FNEPheRj0VkvIjkiMjbIjJLRF4Ub6pkEblSROaIyCci0uC6H1Zdb4wxxkTXANxcBeNwI4lexc3W+RNVXSQiFwE/EpHf4IYvTsLNlfCSiMxS1dXBEraSvDHGGEP0SvLecNgkYCtu8q+NQDtVXeS9/hYwFDcJ1X9VtcybFfOvNLBUt5XkjyOSkKqSFPJ05eYYGti3W7SzYIJIT7Z1WVqqRYsW7lPVTtHORwg6isgCv+dPq+rTNU9E5DLc6n+9gCzgBOC7ddKoxs1z4T8F8h6gc30ntiB/HJGkDJIHXRPtbJgA/vHyzxveyUTF6L7top0FE0RqomxtssSad8a7fQ2Mk5+Mm3WwGjggIhNwk3X5i8NdCPT329aV2imPA7LqemOMMSa6lgPngltdEzgHSBW3BDAiciGwCrdOxGXiVpcU4A7g7foStpK8McYYQ1THyf8DeFJEbsQtL/0U8B7wtIik4aaivkNVC0Xkn7jVJxV4QVXX1JewBXljjDHHPSF6a797CzndHuClCwPs+wrwSqhpW3W9McYYE6OsJG+MMcZg09oaY4wxphWxkrwxxhgDzTmELmqsJG+MMcbEKCvJG2OMMWJt8sYYY4xpRawkb4wxxmAleWOMMca0IlaSN8YYY7CSvDHGGGNaESvJG2OMOe5Fc+765mRB3hhjjAGbDMcYY4wxrYeV5E2TG9SnC6NO6EWX9hlUq5K7r4DPlmxi2+4D0c5aQDmd2jJmWG+6dckiKSGBHbkHWbFhF2s27Yl21ppceVkJh/bupKykiKqKchJTUknPbE9mh67Exdk1f6xRVfbl5bF7906Ki4qIj48ns21bevbsTWpaWrSz17LE6GQ4FuRNkxARvnLxKTzwjcn079k54D4LVmzhZ09O5+N5axtMr2Txk43O0+68fIZe8hClZRUBXx8+sBu/+PblTBo7iPj4owPcolXb+OOz7/PGB4sbnZdoK9ify8Zln3MgdzuoHvV6QmIy3QcOp9fgk0lISg453SWzprF/15aQ989o35lTJn+5wf1WzX2f3ZtXH7V9yNhzyOk7NOTzHc9WrVzBZ7NnsX/fvqNeExEGDR7K+ImTaNe+fRRyZ44VC/Km0dplpvGvX3+Nc08fUu9+o0/szdt/u4c/PPseP/3TtGbP16+efidogL/16jP4/X1XkpyUGPT4k4f25IXf38Lz0+Zy189foqKyqrmy2mxUlU3L57Fl5fx696usKGPLyi/Ys2UtI8ZfTJusjiGlf/jQ0QGksfbv2hIwwJvQVFdX89b0N1m1YnnQfVSVNatXsmnjei685DIGDbYLJ7CSvDFHSU9N4q2/3cNJQ3oAUFJazr+mzuWtWcvZtvsAKUkJDBvUnRsvPZXxowcAcN/XziMhPp4fPvrfoOk+P21u2HnpldPBd46N2/J47s3PAu53xTkjeewH1/iqp9du3sNTr3zK4tXbOFRYQs/s9lx+9ki+fMFoUlOSuOHSU0mIj+PrP/l32HmKtjVffMSujSt9zzt260OXngNJb9ueuPh4SosOc2DPNnZuXEFVRTmlRQUs/OB1Rp93NemZ9ZfwKspKKCs+DEB62/Zktu/SYH5S0jMb3Cd//x6y+9ReMBYezGuWi4lY9fb0qUcE+BOGDWfI0BPJateOqsoq9uzexcIF89mbu4fy8nKmvvEaX7rqWvoPHBTFXJvmYkHeNMqTP7nOF+BXb9rNVfc+xabtR/4gL1u3kxemz+OGS0/liR9fS3JSIvfeeDafLFjPO5+uCJjubQ9OCTsvrz52u+/xz//6FpWV1Uft0zO7HX9/5AZfgH/mjTl8+9cvH7Hvmk17eG/OKv7y0kxeffQ2enfryHUXncKytTt57PkPw85XtGxft9QX4OMTkzjx9PPpmNP7iH3SM9vTIbsnvYaczIrP/sfB3B1UVpSx9JMZnHrBV4iLjw+afuHB2r9zt/7D6DFwRJPku++wU494vmn5XAvyIVq1cgUrVywDIC4ujsuuuIqBg4+sYevcpQsnDh/BR++/y8IF81FV3poxldvuuPu4b6ePxZK89bQxERs1tCdfvnAMALv2HuLC2584KsD7e37aXO76xX98z3/x7cua7J/qlGG9uXjCMACWrdvBK+8uDLjfD2+7gPRU1+Y8Y9Zy7vr5SwEvBgBWrN/FxXf+mfzCEgB+cOv5dGzXpkny29wqy8vYtNyrDRFh+BkXHhXg/SWlpDFiwqW07dgVgJLCQ+xYv6zecxw+lOd73KZth0bn2TSOqjL7k499z8+ccNZRAb5GXFwcZ593Pr379gOgtKSEhQvqb9IxrZMFeROxr15aW+L68eNT2bOvoMFjXpg+j4/mrQFgaL9sRp/Qs0ny8vDdl/geP/TkDDRA57JunbP4ykVjAThcXMY3H36hwXQ3bsvjB4++AUDbjFS+//XzmiS/zW3XplVUlpcBkN17CO27Nvw5x8cnMGTsuYjE+dKoj39JPj3Lgny0bdm8iYMH3AiWdu3aM2bsafXuLyKceto43/MN6xruEBvzpJluUWRB3kRs4piBgAuYb7wfeg/056fWtrdP8NJojEljBzPxFNee+PmSjUGbAC6eOJzERFf9/K83P2PfwcMhpf/8tHls3bUfgGvOH90qqvT2797ie9xjUOjV6OmZ7cjq3A2Aovz9lJUUBd23pgo9KSWNpOTUiPJpms6O7dtITU0lNTWVU047nfh6mlpqZOd08z0+eOhgc2bPRIm1yZuI5XRuC8DG7XmUV1SGfNyKDbt8j7t1zmp0Ph6++2Lf458+EbzX/kVedT7Av6eG3rGvqqqa5978nAfvvJiuHTMZd3I/Zi/cEFlmj5HCA64qPTk1nYx2ncI6NiOrIwdztwNQVnyY5NT0o/aprq6iqMCVGq2qvmU4c8JZnDnhrLCOCVTjdTxrDRfw4bKSvIlYYoIrKVRXh/dDUVVV2waekNBwaaM+l00awegTewPw7pyVzFm0Mei+Y4e7/XL3F7Bs3c6wzvM/v9qBSWNbdi9kra6morwUgPQIArD4TYpTVRX44q0o/wBa7f6OVlXfeu3Yvs33OCurXRRzEn0i0my3aLKSvInYvoOH6ZHdnj7dOxAfH3dE8K7PwN61Q6125+VHfH4R4Wd3ulJ8dXU1Dz05Pei+OZ3aktnGVSnPW7o57HMtWbODgsMlZLZJZWjf7MgyfKyIMOa8awFITE4J+/CSotq+FUkpgXtbH/Zrj7eSfOtUWVnJpzM/8j0fOGhwFHNjmosFeROxRau20SO7PVkZaUweN5S3PwncFl5XTY98gOVhlqj9XX/xKQzt5wLuG+8vZsmaHUH3Hdin9sJi4/a8oPvVZ/22PEYN7cmQfi07yIsImR0aHrMeSHV1FQdz3ecYn5BIWkbg5pRCv571EhfPrk2rKC44SEV5GQkJiSSntaF9dk+7AGihioqKmD71DXJz3dTNqWlpnHTy6CjnKvqiXepuDhbkTcSmzVzGZWePBOBX917Opws3UFhUWu8x554+hMu9Y4pLyvlgbmQzmyUmxPPj2y4AoLKyiof/8la9+2d3aut7vGlHZGOuN27by6ihPemZHbvTgO7ZspaKMjdksFP3vkF/9PxL8qvmvhc4scWQ2aELA04aT1anln1hFOuqq6spKy0lL28vG9avY9nSxZSVuv/V+Ph4rrjyGtLSj+57YVo/C/ImYq/8bwE/uf1C+nTvyKA+XXn98du59rt/52BBccD9zxo7iOd/83Xf89ffX0RJaeBpZxtyy5Xj6NPdTb3672lz2bBtb737t0mrnY+9Ztx7uA55x6UkJ5KQEBd0fH1rVVZSxIYlc3zPeww6Kei+hSFOTlOwP5dFH77GwFET6D5geKPzaCLz5uuvsD7AELmsrHZcesVVZOfkRCFXLY+V5I3xU1lZzTd++m/eefpbJCUmcOaoASx6/Sc8/u8PmVEzrW1yAsMHdeeGS07lKxef4lsIpqqqmt/9M0gJsAGpKYncf8tkwE2j+6un3mnwmDaptUG+uLQ8ovMWFZf5HmekpQS9mGmNqqurWfHZ/3yl+Jy+Q8lsH3ihodKiQirLS0lv256OOX3I6pRDetsOJKWkgghlxYc5mLuDbWsXU1xwEFVl7YKZxMcnkt23/vUNzLEVFx/HwYP7LcjHMAvyplE+W7KJr/3oX/zzFzeSkpxI146Z/Pq7V/Dr715R73H/mvp5g6XvYO66bqKv+v2pVz5l595DDR6TnFy7EE2wRWsaUlpee1xaSlJMBfl1i2ZxaK/rH5GSnsmAk84MvrMIYyZ/OehFQFpGFmkZWeT0Hcq6RZ/4Zs5bs+Aj2nbKDtrOb5pPj569UIXi4iL25e2lvNxd6B7Yv5/pb77BmlUrueTyK0lMDL5g03Eh9gryFuRN473xwWI279zHH++/itNG9mtw/9z9Bfz4sTcjOlfbNql856ZzAFft/vtnQqsNKPML7MlJkX3tU5OTfI8jrQ1oibatWcTO9W5Bk7i4eIaNu6De5WZT0tqQktbw9L4SF8eg0ROpKCsld9s6qquq2LxiPiec1jpmDYwlY8ae5psBT1XZuWM7SxYvZOVydwG2ft1aXn/lJa657qu+dR1MbLAgb5rE4tXbmfS1Rxk2sBtnjx1Mj+x2xMfHsTsvn6LiMn77vS/5fjzu+91rvvbtcH3npnNo39Z1EHp8yoccyA8+I5u/wyW1Ve2pyZGVVtJSa4N8YXH9HQxbiz1b17F+8Wzf80Fjzoq4Z34wA04eT97OTVRXVbJ323oGj5lEfIL99ESLiNC9R0+69+jJiJEn88ar/6G0tJStWzYz7/M5nDaunlqcGGdt8sY0YPm6nUcMi0tIiGP2lPt9Af6N9xfx2nuLIkq7U7s23HX9RAD2HijkiSkf13+An8P+7eltwh87DtA+040ZLy2riIlOd/t3bzuiZ3yvIaPI6dv064onp6bRIacXeds3Ul1dRf6+XSHNpW+aX4+evfjS1V/mpSn/QlWZ+9lsRo0ZS1JSUsMHm1bB6mVMs/rBN85nxKDuAOzZV8C3fvVy5Gnder6vl/zv//nuEYG7If6L5/TKjmzsdt8ebnrY7Xta/xzf+fv3sHz2W75Z67r2HkS/Eac32/n815ovOdzwQkbm2OnRsxcDvIlwysvL2bB+XZRzFCXSfLPeRZMFedNshg/sdsSqbd/65X/Yfyi06vW6ema345Yr3YpZ23cf4OlXZzdwxJHWbc71Pe7dLbIg37+nC/Kr/Obeb42K8g+wdOatH5rEAAAgAElEQVQ0qipdP4UOOb291eea78coMam29qSmB79pOfr3r10oKnfP7ijmxDQ1q643zSIhIY6nH7mBpET3FXthxjymz6x/ffL6/Pj2i0hOcm3pv3jq7bAWxAHYufcQhUWlZKSncPLQ8KuKTxrSwzct7sqNrfdHsLSokMUz3/TNbd+2YzbDxl3Q7J2tKitqa13iE47zHtwtUJuMDN/jsrLY6G8SLgFisEnegrxpHg/cUltNvzP3IN/77WsRpzWwdxeuv8hNhbt28x6mTJ8XUTpzl27m3NOHMLB3F3I6tWVXGPPmn3t6bVv1R3PXRHT+aCsvK2Hxx29SVuyW2E1v24EREy4JKeiqqq/kL3FxxMeH99Nx+NB+3+OkAKvamcZZtXI5mze5xZnGnjaOjh3DW3mwtLS2diUtNfB6BbEv+lXrzcGq602TGzawG/ffUltNf8fDL5J/OPIq2ofuuti3Wt3Df5kR9qp3Nd7+ZLnv8VWTR4V8XFyccNNlpwJuQZ3PlmyK6PzRVFlRzpKZUykudP0JUtIzOWniZUdUo9envKSIWa/9jVmv/Y15b78Q1rmrKiuPWN8+q5NNvNL0hBXLlnq3JWEf7b8aXYcwLxBMy2ZB3jSphIQ4nn74q75q+n+8NpsPPo9sfnqAkYO7c9mkEQAsXLWN/34Q/g9YjRkzl1FZWQXAnddNICEhtK//NZNH+Trd/eftL1rdGtzVVZUs+3QGhQfc5EOJyamcdNblJIcw1r1GclobUtIzASg5nM+hvND7JWxft4QKrwo4s0OXgOvTm8bp27cfCd6wxKVLFlNWFnqn1JLiYlatqJknIY6+/Qc0Sx5bA5HmuUVTzAd5EZkoIioivaOdl+bgvbebo52PGg/ccj4jB/cAYPOOffzg/95oVHoP332pr734wSemNSqtHbmHePGtLwDoldOBh+68pMFjunfJ4g/3Xw3AocJi/vDs+43Kw7Gm1dWs+OzdI1aWGznxsohmncvuUzsl7bpFn1BdVdXgMQf37mTT8rm+531OOCXs85qGpaSmcsKJbm2A0pISZn4Y2ve0urqat6ZPpdRbrGb4yJNITU1ttnyaY++YB3kReUhEthzr85rm519NX11dzW0PTaGoJPKZ4cad3I/zxrm28FlfrOPDJmgL/+VTb1Ps5ek7N53Nd73Z8wLp37MzM/56Nx2yXMnzF399O+TJd1qKNQs+Jm+Ha6uNi4tn+PiLg05H25AeA0eQmOwCQOGBvSyb/ZavA18ge7asZcnMqb5hep2696Njtz4Rnds07IzxE0lOcc0vSxYv5JOZH1FdHXw+h+KiIl5/5SU2bnBD5tq0yeDM8Wcdk7y2VLE4hM463pkmUbea/i8vzWL2wg2NSvORuy/1PX7wyemNSqvGtt0HuP2hKTz/268TFxfHL++9nMsmjeCZ/37Gqg27KCgqpXuXdlw8cThfvWSsb1z+lOnz+PNLM5skD8fKhiVz2LVxpe95SnoGezavYc/m0C+WMtp1oscgtzRwYnIKQ089l2WfzEC1mv27tvD5jOfp1n8Y7bv2IDk1ncqKcgoP7GX35tXk76sdhdAmqyNDTz236d6cOUqbjAwuvOhS3nzjVVSVz+d8yvp1azhx+EhycrqRnt6GquoqCvLz2bxpA8uXLvHNYZ+UlMRV115vy83GIAvypkncfvV4XzX92s17+Gkjq9bPGzeU009y8+BP/3gp85ZtbnQea7z23iI6ZLXht9+7guSkRE4Z3odThgcvYf7rzc+5+5cvNdn5j4VtaxaxdfXCI7YVFx6iuLDhxXz8VZSX+YI8QMec3gw74wJWzX2fyopyKspK2LJyPltWzg+aRoec3px4+vkkJNosas1t4OAhXPalq3h7xjTKy8rYl5fXYNV9+w4duOKqa8PukR9zWkD7eXOIapu8iDwnIjNFZIKIbBaRAhHpGWy733GpIvKAiCwVkRIRyReR2SLyDREJ6T2JSLyI3C4i80TksHdbICL3iUhKnX0/FZGgRUkReVlEvgiw/XIR+dDL32HvXF/3XntTRJ7z2/dmEVHv8aki8o6IHBKRfSIyRUS610l7S83+wLNe2/xDobz35vD312bz67+/Q3FJObc9OCXild5qzPpiHff97jV25h7koT/PaKJc1nrqlU+YcOMfef+z1VRVBa7SXLx6O9fd9w/uePiFVjWN7e5Nq4+Yj76pderej7EXfIXsPkOJi4sPul9G+84MO+MiRk641AL8MTRo8FBuufWbjBh5sq8zXiCZbdsy6dzJfO0bd1iAj2FyrHsKe4HoZlXt7QW5U4EOwBPAUlWdGmy7d3wH4F2gI/AcsB5IB04HvgzMAS5W1RJv/4nAx0AfVd3ibUsB3gROBp4FVgCJwCjgBmAzcI6q5nn7XwG8DgxS1fV13k93b/8bVfUlb5sAfwNuA6YC04EiYARwK/ABkAYcUNWbvWNu9vLyFe/YfwLzgd7At4ByYLiq5nv7n+el8V/vM/oIWKOqQeti49I6a/Kga4K93CQ6ZKVHPKtdIHFxEvGQuVDldGrLKcP7kNO5LcmJCezce4jl63ayetOeZj2vv/df/vkxO1dTqqwoJz9vF8WH86msKCchMYmklDSyOuXETC/60X3bRTsLEauoqGD3rp3s37eP0tIS4hMSSE9Lp0t2dkwE9tREWaiqo5skreyB2udrTzZFUkdZ/evJTZbPcLWE6vpBwIOq+kiI218A8oHxquq/oPfTIvJ74D3gUeCOes75GJADnFATyD3PiMivcBcRzwPne9unAptwwfaeOmndA+QCr/ptux+4BbhWVV/x2/4fEXkU+B8wzHsvdT0OTFDVxTUbROQlYC1wJ/BrAFV9z3sNYJGqRrZ2axNrygAPNHuAB9iVl8+bH0Y+NO94lpCYRIec3kQ2UbBpbomJifTs1ZuevXpHOysmSlrCELpq4C+hbBeR8cA5wHeBJBHJ8r8BO3FB8BYRyQx0Mm8o3a3AA0BFgDSKgB8Bk0VkCICqVuOC780i0tYvrTQvrSdUtdJv20+AP9QJ8Hhp7QWuBoJFr9/4B3jvmC3ANGBCkGOCEpHbvGaIBVppc4YbY0wwNk6+eWxT1X0hbj8PiAeWAAeD3J7A1VCMJLBzcO/77XrSmOrtO8bvuGeAClwJvcZNQDLwtN+2cUAb4O9Bzo+qbgQ+DfJysPlfNwPZwdKs51xPq+poVR0tCTb+1Rhjjictobo+UIAPtr0zsAq4K4R0VwbZ3hk4AFwZQhq+9ndVLRKRp4G7ReQxXEn8W8Bzquq/9mhNQ9c26rczyPbcINtLgIwgrxljjGmkaI9pbw4tIcgHq0MOtD0PaKeqMxtxvjwgE/hCVcNtQH4C11RwGVCG6zdwWZ199nr3PYGN9aTVncAXAsHmo1RaRs2LMcbEnhZQtd4cWkLQCDY2KdD294BsEQk6LZOIfFtEflPP+T7Ave+g3cxF5EoR+ZeIHDE+SFV3Aq8A93q3t1R1XZ3D5wAFuJ71wdLvC5wR6DVtbROjG2OMabFaQpAPmarOAj4E/iEiPeq+LiKXAL8Bgi74raqbcUPv/iAiwwKkMRbXxn5AVQNNzv1HYDxwLq4Xf930S4BfAN8TkaMuJESkM8Hb3Y0xxkSBW0/eprVtCa4H3geWishTuE547YDJwKW4AP1EA2ncgxt/Pk9E/gF8jhtrPx64FjfE7YeBDlTVxSIyC9ds8FGQ9P8ADABeFpHrcT3ji3CdAW/FjW3fHsqbDUEJMFFEDgHTvJEAxhhjTOsL8qq6V0ROw1WXf9m7LwKWA9eo6ushpFEsIpNxVeo343rMVwBrcGPRnwtSiq/xRwg+NNircr9NRN4B7saV+BOApcCdqvqKiDTVuPbHcJ/BpUBX3KQ5xhhjwhL9UndzOOYz3pnoORYz3pnItNYZ744HrXnGu1jXlDPepeUM0gG3BpqypfGWPXLOcT3jnTHGGBN1MViQb10d74wxxhgTOivJG2OMMcTmZDhWkjfGGGNilJXkjTHGmBid8c6CvDHGmONezWQ4scaq640xxpgYZSV5Y4wxhtisrreSvDHGGBOjrCRvjDHGYG3yxhhjjGlFrCRvjDHGYG3yxhhjjGlFrCRvjDHGiLXJG2OMMaYVsZK8McaY456b8S7auWh6VpI3xhhjYpSV5I0xxhgkJtvkLcgbY4wxWHW9McYYY1oRK8kbY4wx2BA6Y4wxxrQiVpI3xhhjxNrkjTHGGNOKWEneGGPMcc9NhhN7RXkryRtjjDExykryxhhjDFaSN8YYY0wrYiV5Y4wxButdb4wxxphWxEryxhhjDLHZJm9B3hhjjLHJcIwxxhjTmliQN8YYc9wTbz355riFlQ+RZ0Qkq6nelwV5Y4wxpgUQkcuBDap6SESGisjHIjJTRP4iIonePveIyGwR+dTbv17WJm+MMcYQ3TZ5EWkL3Axc6W36A3Cjqm4XkbuAr4vIh8BE4EwgEZguIp+q6v5g6VpJ3hhjjIm+XwN9gfdEZARQpKrbvdeeBs4BJgHPqlMOvAhMqC9RK8kbY4wxQFyUivIi0gPIAUZ7938H1tW8rqoVIpIAdAAW+x26B+hTX9oW5I0xxpjm1VFEFvg9f1pVn/Z7fgrwilc63yIihUDXmhe99vgqYD/Qye+4rsDe+k5s1fXGGGMMrk2+OW7APlUd7Xd7us6pNwLnuTxIWyADSBaRbO/1W4H3gY+BG7z9koDrgJn1vScryRtjjDFRpKpLRGS9iHwGlAH3AyXACyISB6wE7vWq7eeKyKfeoX9U1QP1pW1B3hhjzHHPlbqj171eVX8J/LLO5kkB9nsceDzUdC3IH0f6983hLy8+FO1smADueWlxwzuZqPj8x0f9zhrTaliQN8YYY4C4GJy73oK8McYYQ2yuQme9640xxpgYZSV5Y4wxBltq1hhjjDGtiJXkjTHGHPcEt9xsrLGSvDHGGBOjrCRvjDHGEJtD6Kwkb4wxxsQoK8kbY4wxIjZO3hhjjDGth5XkjTHGGGJznLwFeWOMMcc9AeJiMMpbdb0xxhgTo6wkb4wxxhCb1fVWkjfGGGNiVNCSvIhcH05Cqvpi47NjjDHGREcsDqGrr7r+1jDSUcCCvDHGGNOCBA3yqnrWscyIMcYYEy0isdkmH1bHOxHpAJwOfKSqRSISp6rVzZM1Y8yxNGlwJ1buKiC3oCzaWfHplJHEsG5t6ZKZTEK8kFtQxobcw2zaVxztrBnTKoQc5EXkXuAhIB0YJCLlwMcicouqftJM+TOtVEVZCQV5uygrKaK6soKE5FTSMtuR0b4LEte6+nuWlxSxavZbjDz3mmhnpdn06ZjGL780lMpq5U8fbOTVBTuD7rv4wUmNPl9eYRmX/OlzyioDlxEGdmnDt8/px9i+7YkPsGrIql0FPDtnKx+symt0XoypEYvj5EMK8iJyKfA9YCSwHEBVd4jIQ8A0EZmkqouaLZem1Sg8kMu2FfM5lLsdVI96PT4xmez+w+g2aCQJSclB05nzyp8bnZfElDRGXXgD8QmNGym6YcHHVJaVNjo/LVWcwMOXDSElMR6Avp3Smv2cT8/aHDTAXz26G/dNHkBSQvCLwaE5mfz+6mFMW7Kbn09fQ2X10d81A5WVlTz7j79xYP/+o167465v0zYrKwq5MsdSqL9+3wMeVtUt/r0PVfUFERkA/By4qBnyZ1oJVWXbyvnsWLWg3v2qKsrYsXoBedvWMWTchaRndWi2PPUYOqbRAX7PplUc3L2V5LSMJspVy3PzuF4M694WgB0HS3j8g4317j9tye6wz5GTlcLo3u0A2HagmDcXB07jnCGd+MGFA30lqs37injli52s3lVAYWkl2VkpnD2kMxcM60JKYjyXjswmPk74yX9XhZ2n48HsT2YGDPAmsNgrx4ce5EcRvLf9K8C3miY7prXauGAmuZtrf2jb5fSmU88BpGW2R+LiKS8+zMHcbeRuWkVVRTllRQUs//i/DD/7StIy2x2VXufeg8POQ2lRAQV5uwBIaZNJl75DIn9DXnpblsxuVBotXb9O6dw+oQ8A1ao8NHU1pRX1d7N5cOrqsM/z2JeH+R7/9ePNAUve2W1TeOTyob4A/8aiXfz6rbVH7LtpXzFzNhzgpXnbefTLw+nWLpWLhndl7Z5Cnv98e9j5imVlZWWUlBRz4vARvm2bN26gqKgoirkyx1qoQb4KCPafXwbEN012TGu0e/0yX4CPT0xi0Knn0S671xH7pGW2I6trD7oPOpm1c98lf+9OqirKWD37LU6afB1x8Ud+hQaccnbY+Vg1+y3f454njCUuLvKvpaqy4YuPqKqsiDiNli5ehEcuH+KrFn/li50s3Hqoyc8zrFsmEwZ1AmDdnkLeXZEbcL/bJvQmNcn9zWatzePn09cETXP93iLunLKEKbeOISMlgVvH92HG0j0cLI7dv1e4kpOTueCiS4/Y9uLzz1mQr0csjpMPtQfUZ8BpQV67Gvi8abJjWpvK8jK2rZzvnogw+PTzjwrw/hJTUhl65sVkdOgKQOnhfHZvWN7ofBTu38PBXVsASGvbgY49BzQqvd3rl5G/N3jns1jw9TN7MTQnE6ippt/QLOe5++x+vsdPfrSJQK3nnTOSuWi4+04Ul1fy8LTgAb7GtgMlPPreegAyUhL4+hnBv3fGNMQtUNM8t2gKNcj/DHhQRAbhJr5REYn3etz/yHvdHIdyN6+mstwNuercaxBZXXo0eExcfAL9x0xCxH399m4Jv/q3rq3L5/oe9xp2aqOuyEsKDx2RXiwa0DmdW8f3Blw1/cPTGq6mj8TYvu04pY9rjlmy7RCfrg/cPjxxcEcS49334c3Fu0MukU9bsoddh0oAOH9Yl5hsUzWmMUIK8qr6BXAH8BZuCN2bwD7gu8BVqjq/2XJoWrRDe7b5HucMHFHPnkdKy2xHZqccAIrzD1BeEnkV4qE9232l7owOXWmf0zvitLS6mvXzP6C6qjLiNFq6hDjhkcuH+oLqqwt2smBL01fTA9w9qbYU/8SHm4LuN2FgR9/jqUE65QVSperrxNexTTIn97Le4iZCIkgz3aIp5AHLqvoBMASYBPwGuALop6rvNVPeTCtw+KAbp5yUmk56VscG9j6Sf8/6skYE+a0r/Erxw4O1KoVm59rFFO53bcZJqemNSqul+sb43gzOdqMFdhws4fH36+9NH6lJgztxYjfXHDBn/X4WbQt+ITG8h+vdv/9wOetyD4d1nk/X7fM9Htv36E6cxhzPwhpfpKoVwKxmyotpZbS6mspyN348LbN92MfXVNcDEZec9+/YyOEDewHI6tqTtl7tQCSKDu2v7V8A9B99Fqs+nRFxei3RoK5tfG3XNdX0JRVVTX4eAe48q7bX/pMfBb+Q6JSRRJtk91O0dHt+2Odas+cwh8sqaZOcQN9OsXlhZo6NGOx3F9aMd8nAD4HrgG7ADuAF4LeqWt482TMtmgjDz7kagMSklLAPLy0u9D1OTE4N+3hVZeuKeb7nvYadGnYaNaqrq1g//wO02rVLd+k7tN4OhK3Rsaymv3hEV/p1bgPA+yv3smZP8NJ5n461gXn7wcimq922v5ihOZn0syBvzBFCnfEuDfgQSAZ+AKzDVd3/DDhPRM5V1didEswEJCJktO8c0bHV1VUUeO3o8QmJpGaE35aat3UtJQUHAejQoz9t2nWKKC8A21d+QdEhV+2bnJZB7xHjIk6rpbp9Yh8GdnGBd2czVtMnxAm3eWPvK6ur+cvHwdviATpl1M58uONASUTn3La/hKE5mWRnhX+xaUyNaLefN4dQS/IPAqXAmapaU6+6UkSmAx8AP/FuxoQkb+s6KsrcD3r7bn3C/ueqrqpi28ov3BMRep04NuK8FB7IZcea2lmZ+485i4TEpIjTa4kGd23DTaf39D3fcbCEP391BL07pJGWHM/+w+Us2prP1CW7Gl26v3JUDt3buZqZaUv2sK2BwJ2WVDufQWFpZM02hWWuN35yQjwJcWLT3BrjCTXIXwvc6BfgAVDVMhF5EHgGC/ImROUlRWxdVju1Qs7AkWGnkbtpJWVFBQB06T0kopoAcH0B1s/70DfPftf+w0IaBtiaxIvw4KVDfNX0AGP7HtmHIicrlZysVC4e0ZX3V+7lwamRtdWnJMRxy5m9ASitqOKpmZsbPCbVL8hHOoyvuLw2r2lJ8RREeLFgjl814+RjTahBvguwNchrm4DI6mzNcUerq1k79z1fKb5znyFhV7NXVVawffVCAOLi4+lxwpiI87Nl2VxKCl2Vf0qbTHo3snd+S3T1mG6+3vQ1CkoqWL6zgIKSCtqnJzG8e1tfsD33hM50a5fC7f9ezOGy8AL9dWN7+KrfX/liJ3sLG162NtlvIZpgi9Y0pNzvuJREC/LG1Ag1yK8BBhM40J/gvW5MgzYt/tQ3v3xyegZ9Imj73r1+GRWlroNW137DSE5rE1Fe8vN2sXvDMvdEhP5jziY+ITGitFqqtKR4bpvQ2/f8cFklj72/gTcX7abKb5XA5IQ4rh/bgzsm9iEpIY6hOZn8/PKhfOfl0GcjbJOcwE3jXJNAYWklz8zeEtJx/oE9KSGyolRygn9tQNOPFjDHh1hskw91nPz/AQ9JnU/A63H/C+BXTZ0xE3t2rl3Cno0rAJC4eAadNrne5WYDqSwvY+faxYCbJ7/7kFER5aWqopz182ur6XMGDG/U8LuW6spRObRLc/0L8ksq+NozC3l94a4jAjy4QPvsnK3cOWUJZZUuSE4c3ImzBoc+98FN43rSNtVdJE35fBv5JaGVpkv8qtr9g3U4UpNqf8r8q+6NOd4FDfIicnrNDdgI5ANfiMjVIjJGRG4AFgNrgV3HJrumtcrbtp4tS+f4nvcbNYGM9l3CTmfn2sW+aXRzBo4kMTmy3tSbl87xtemnZmQ1avhdS1YzHzzAw9NWs2Fv/ZMOLdx66Ihe918bF9owwnZpiVw/tjsAB4rKmTI39BXh/INym+TIgnxmiru4KKussk53JmLSTLdoqq+6/oUg239X5/lYYArQt0lyZGLOoT3bWT//A9/zboNPpkuf8JeBLS8tZtf6pYAbVx/ONLr+Du7eSu4mb1lcEQaccjZx8Y1bd74lap+eyABvyNzaPYV8vGZfA0c4ry7YydfO6EWnjGRO6JZJu7TEBueSv3V8b9KS3Gf4z0+3hFWa3ufXbp+dFf58CQA92rvj9uQ33AfAmEBE8C1zHEuC/rKpap9jmRETmwr357L6s7d9k8x06jUw4lLzjlULqK50VcDdh4yKaJhbZXkZGxZ87HvefdBJvhXxYk3/zm18P1pzNgReGCaQympl/uaDXDS8K3EiDOjShvmbDwbdP7ttCleO6gbA7vxSXl0Q3up9m/fVToDTLcJx7j07pAGwYW94U+IaE+tir/hiWoziggOs+nSGLzC3y+7FgDFnR9S5pbSogD2bVgKQlNaGrv1OjChPmxZ94lsMJ61te3qccEpE6bQG7dNrL4JyC8Ir4eYW1M5t1S6t/s6It0/o41uT/qmZm6moCq+6fG9hGUVllaQnJzA0J6PhA+oYkp3hmxZ3YwPNEcbUJwYL8iHPePcsBFwG2kdVv94kOTIxoayokJWzpvvmts/o0JVBp01G4kJeE+kI21d+4asN6Dl0DHHx4bfd7t+xibxt63zPi/MP8Pnrfwvp2LLiQua88mff88HjLqBDt5bdQnVkh7bwPvfUxNDGrvfukMZFI1zfis37ipi+NPQV5Pwt3Z7P6f070LtjOp0yksgrDH2m7NP71475n7vpQETnNyZWhfqfvwQ3X/2VQAGw0Lu/EugELG2W3JlWqaKshJWfTKO8xFWdprVtz9AzL4p4eFpxwUH2bl0LuE5ynXsPDj9PpSVsXDgzovO3Vrvza0vjQ7Mzwzp2iN+4+j0FwWesvmtSXxK8C7e/fLSJSPu8feK3ktzkE0LvkBkncNlINyoir7CMJdvCX+DGmBqxuNRsqNX1S4DvA8NV1TdWXkQeA2YCDzV5zkyrVFlRzqpPZlBS6KZGTU7PYOj4S0mIYAGbGttWzPMNdet54tiIagMO7d0R0YIze7e4KSDiEhLo2L2/b3tyWvjVysfa+tzDHCgqp316EuMHdQipAx1A307pDOvut/RrkMVlBndtw6QhbiKjVbsK+GB1XsR5nbl2H/edP4CEuDiuG9ud/8zfEVIv+ckndvF1unt7+Z76qxuNOQ6FGuQfBn7sH+ABVHWLiDyCW1/+3KbOXCwSkZuBZ1U15lp/qquqWDPnbQ4fdEu/JiancsL4S0luxLrshw/msX+HG9KV3q4THbr3iyidTj0H0KnngLCPqwnyiUmpDDjl7IjOHS0KTFuym5vH9SItKYEfXzSI+15dUe8xCXHCTy8eRLw3v+fUJbuCBs67z+7n69j3xIf1L0LTkNyCMt5amstlJ2WTk5XKnZP68qcP6l9Ap0tmMvefPxCAwtIKnp0dbFJOY0ITi23yoRaJRgNzgrw2Gzhmc4GKyEQRUb9bhYgUiMgmEXlXRL4jIll1jukkIltE5N1jlc/jjVZXs27ue+T7rSw3dPwlEc8pX2Pr8rm+x72GnRr1qq/W5rk5WzlY5Nq3zx7amUcuG0JSfOB/+zbJ8Tz65WGM7On+ZnmFZUED58k9sxjXvwMAX2w+2CRt4U/N2uybL/+m03sesaBOXT3bp/LXG0aS5XUK/OvMzSFPvmPM8STUknwJEGzu0E5AZItAN87tQB6QCGQAOcApuNn3fioiX1PVqd6+aV4+I1vH0jRo46JZ7N/pSnMSF8/gMy5s1NKv4KadPbRnGwCZnbrRrmvwH30TWH5JJd9/dQV/vWEkifFxXDIym9G92/GfL3awaOsh39z1p/Rtx7Vjuvt65JeUV3HvS8uCzl1/99m1nQ6f/KhplqzdnV/KQ1NX89urTiROhHvP7c+kIZ3476JdbNhbRFFZJV0yU5g4uCOXjOjqG5c/fRqeDB0AACAASURBVMluXpq3o0nyYI5fghxf4+Tr+B8uqH4zwGt3eK8fa++p6pa6G0UkB/gb8LqIfElVp6nqVhHpgQX5ZrFl2ee1k8vg2uHztqwlb8vakNNIz+p41OQ2dUvxJjILtx7inheX8rurTiQzNZHsrBS+c27/oPvvyS/lvleWs2p3YcDXx/Vvz0leaf/jNXks21HQZHl9b+VestLW8r3zBpCUEMfw7m0Z7vUPCOTNxbv45YzQv2fGHG9CDfI/BhaISCnwM1UtFJFM3Lz15wGRLwPWxFR1l4hcAUwH/iEi/VS1EHgD2ALcDCAi3YDHgUlAGfAP4BeqesSAYhG5HLgH12QRD6wEnlLVZ0TkTeCQqtakWTOX/3VAW2Aq8H1V3S0iE4GP/dKtaers43+xIiLXAHcCJwFJuCmF3wD+T1UP+e13M/As0B14FRgFfFVVX43sk4vMzrVL2Om3FjtAaeEhSgvDW5O8fU6fI4L8wd1bKdy32/daZsfYnLDmWJm36SDX/G0+d0zsw4XDuvrGtfsrLK3ktYU7eW721npXcfti8yF+97913HR6T/78UePa4gN55YudLN2ez7fO7sfYvu19/QP8rd5dyD8/3cKHjejsZ8wRJDbb5EMK8qq6TUTOxK0bf1BEcnHLz84BzlTVbc2Yx7CpapWIfBMXIL8K/DXAbm/gqvq/jQuU3wUqcZ0M8Rbj+RtwGy5YfxcoAkYAvxOR8zi6T8MjuIuIh7y07gJeBM4CVgBX4C4q7vEeA+z1O98/gauAfwH/9tI4EVdbcpOInKOq6+uc83VgPe4i5Y2QPqAmsnfLmiPmo29KbTt3o8/IM9i5djE9h41tlnMcb3ILynh42hp+9856RvfOIicrhfTkBPJLKtiyr5il2/ND6tFeXlXNS/N28PL8HREPmWvI2j2HueuFpXTKcMvgds5IJjEhjr0FZazLPcymPJv0xjS9WOzzE/KMd6q6FhgnIr2BbGCHqoa+CsUx5lXRz8LVNBwR5EWkPa79fpSqLvK2PQv4z/15P3ALcK2qvuK3/T8i8iiuiWIYR87xfwHwa1X9s5fmc0CWl599wJs1nQJV9c06WX4AN0LhJFU9opFTRH4JTAP+KyIjVNW/obRYVW8I4SNpcp17D45ozHoo4uITyBk4guz+wyKeQKcpjLvmrqidu7mUVFTx6frQp7kN5lisA5NXWG6l9SZ0/Q03RzsL5hgL+9dTVbeo6uctOcD7WQEEGhxdgCuV+4qIqrpHVSsARCQN+AnwhzoBvmbfvcDVHD0L4G7glJoleVW1TFVzG8qkd74f46r694tIlv8Nt5DRd4ChuIsWf082kPZtIrJARBbkH2j8D/uxFs0Ab4w5vsQ10y2agpbkRWQ9DUxl609VBzZJjppWKXDUslaqWikiPwCeFJHhuH4G/sWFcbjRBH8PlrCqbhSRT+tsfhB4H/hARO5V1eUh5vN073x/8271GQO84/d8YX07q+rTwNMAA08caXOFGGPMcaS+6vpvHLNcNJ9eQMAaB1V9UkTWAf8HrBORb6nq897LNWO/GuprcMRyW6o6V0RGeWku9mYEfKBO9Xognb3784CGpiSr+35CWz/UGGNMUMJx1iavqrOOZUaamoik4tq4/xRsH1V9T0RG4IYGPiMihV5b+V5vl564znvBdKfOhYCqrgMuFpGzgSm4HvnfaSC7NbUIW73jw/H/7d13nNTVvf/x15ulSFFEFBUQERtib7EkKjaMqLHGJN4UbnJjTDOa3CTGeI1JbmJ6uWqKMbnml0RvTOy9gyiWgIqABVCwglQbdYHP74/z3WUYZnZnYWdn97vvJ495MPMt53v2O7N75nOqhwWamVlJtW4uqKaLSJPk/KmpgyJidURcTurR/sls8yOkdvuzy50naRjwgSbSvZ803/+nKsjrBFIfgX9r4noHSbolG7pYeJ3yS4SZmVnFuqg6j5r+TLW9fOuTVCfpIuBC0nz761XXZ8fsW7S5L/AeQEQsI3WC+1o2br34/AHAP0tsL54voDHNpkTEEuDHwDclHVUi3Z2B/wNWR0TrzTxiZma5VvEQunZolKR5pKaUTUhD1XYGTgGGApcCPytz7lnAHyVdATwBjAROI41hb/CzLL2/SzqLNIRtCbAP8FngRgraxyXtAEyQdBtpcpphwAWkxXsKLc2O/xQwLSImZtt/AAwH7pb0V+B+0vvzPlINwxTSkD4zM6uCWkfd1dCRC/nfFzwPUgH8Kmnp2z9ExKPlToyIv2Rt9ueSJpqZDpxa2A8hIgI4W9KdwJeAX5Lu12TgCxFxXTbjXcPxsyQdQ5pM5ypSu/4PgJ8UXf5e4ElSz/0/AROz89dI+jhwO2kK4dNINS0zgYuB32Y1DGZmZhWpuJCX1J9UBT6SFC2/QZpG9c/VyVppETGWFL239LyRRa8bh5Y1c96NpKi91L5Til6PI92fptJbTJqCttS+IM2Qd00F+boauLq548zMrHlSPnvXV9Qmn80Q9xhpLPdw0nSwWwHnS7pVUkeuETAzM8ulSjvefRd4PCI+B6yBNEMcaRKXrUlTspqZmXVYnbl3/cmUWOQlIpaSFmOpZJiYmZlZuyVV51FLlRbyW1N+9repwHatkx0zMzNrLZW2pb8KDKT0FLE7kjrhmZmZdUgCutQ67K6CSiP5v5Cmfl1HtnraD0lrn5uZmVk7Umkh/0NgG0n/jzQX+5GS/hN4AXg3229mZtZhdaqlZgtFRL2k0aSZ3rYDvkFaO/1S4PcVrLJmZmZmbazi8e3ZQii/Z92Z5szMzHIhh03yNa9JMDMzsyqpKJKXtIY0P3xZEVHXKjkyMzNrY5Jy2bu+0ur6wnHwAnoAQ0gLt9QBX27lfJmZmdlGqrTj3eslNr8IPCjp78BHgZ+2ZsbMzMzaUg4D+VZpk/9v4IutkI6ZmZm1otZYPa4O6N8K6ZiZmdVMrReTqYaNKuQl9QQuAh5oneyYmZm1vbxOa1tp7/p7Wbd3fRegF7Ab8BJwUutnzczMzDZGpZH8X4terwGWkAr4yRHR5PA6MzOz9i6HgXzFhfwLEfFYVXNiZmZmrarSQv4BSXtHxIyq5sbMzKwWlM+Ody1ZavYCST2qmRkzMzNrPZVG8nXAh4DTJT0OzAOWU9AZLyLObv3smZmZtQ2Rv1C+0kL+VeDyambEzMzMWlfZQl7SxcDPImJpRHy3DfNkZmbWptI4+VrnovU11Sb/HaBPW2XEzMzMWldT1fWimeVlzczM8iKPkXxzbfKvq/nZAQRERHRvnSyZmZlZa2iukD8UmN8WGTEzM6ulCoLaDqe5Qv6ViJjXJjkxMzOrkc7Y8W4csLKtMmJmZtbZSRoh6dOSBkq6Q9I4SddI2jTbf7qkRyQ9JOmc5tIrW8hHxJER8VZrZt7MzKxdUlqgphqPirMg9SMtCLcZcClwUUQcAfwNuFBSX+ALwFHAEcBISbs1lWal09qamZlZlUiqA34N/Crb1C8ingSIiNuBEcBBwI0RsSJb/fW3wPFNpVvpjHdmZma51qV6He+2lDSx4PWVEXFl0THfA/6HND/NFsDqov1rgP7AmwXb5gIDmrqwC3kzM7PqWhARB5TbKel44BTg/cDmpIJ+dtFhXYCFwE4F27YhrSVTlqvrzcys02voXV+NR3Mi4s6I2D0iRgLnkdaKeV3SngCSRgPPAo8DJ0vqpjTe7xzgjqbSdiRvZmbW/nwbuFJSL+B14JyIeFfSH4EHSTPS/i0inm8qERfyZmZmtKwnfLVExFhgbPZydIn91wHXVZqeq+vNzMxyypG8mZkZogvtIJRvZS7kzcys0xPto7q+tbm63szMLKccyZuZmVU43K2jcSHfiWzaoysf2HnLWmfDSphw4ZG1zoKZ5ZALeTMzM6o6rW3NuE3ezMwspxzJm5lZp+fe9WZmZtahOJI3MzPDbfJmZmbWgTiSNzMzw23yZmZm1oE4kjczs05P5DPqdSFvZmYmUA7r6/P4xcXMzMxwJG9mZgaQw9XkHcmbmZnlliN5MzPr9IQnwzEzM7MOxJG8mZkZbpM3MzOzDsSRvJmZGZ7W1szMzDoQR/LWYUQEC+bPZ86c11m6ZAl1dXVs1rcvQ4YMpWevXrXOnjXjpRdn8vxz0xh94sm1zkqnMn/em8yfP493332XrnV1bLpZXwYOGkyfPn1qnbV2Rrmc8c6FvHUIz06byoSHx7FwwYL19kli1+EjOHzkUfTbYosa5M6as3z5cu6641aGDh1W66x0Gs89O41HHxnP/HlvrrdPEjsM24nDRx7F1ttsU4PcWVtxIW/t2po1a7j91pt4duqUssdEBM8/N42XXpzB6JNOZtfhI9owh1aJ++6+k/fefbfW2egU1qxZw12338qUZ54ue0xE8NKLM3h59kuMPPpYDjjwoDbMYfvkBWrMauCOW29ep4Dffc+92G3EHmzerx+rV61m7pw3mDTxCea9OZeVK1dy8w3/5LQzPsJOu+xaw1xboekvPM+z08p/SbPWdf89d61TwG8/dAf22ntftui/JV26iIULFzJl8tPMemkmq1ev5v577qJrXVf22W//Gua6fXB1vVkbenbaVKZNfQaALl26cPKpZ7DL8N3WOWbA1luzx15788C9dzNp4hNEBLffdjNnn/Mlt9O3A0uXLOGeO2+rdTY6jeefe5YnJ/0LSL8zx35wNPvsu27hPWDrbdhtxO48O20qd952M6tWreLeu+9gqwEDGDR4u1pk26ooj7UTlgMRwcMPPdj4+rAjjlyvgG/QpUsXjh71QYYO2xGA5cuWMWniE22ST2va3XfeztKlS2udjU5hzZo1jB/3QOPrI446Zr0CvtCI3fdg9EknN577wH33VD2P7Z2q9KglF/LWLs2e9RKLFy0CoF+/LTjwoEOaPF4SBx/y/sbXM6e/UNX8WfOmTXmGGdOfr3U2Oo0Xnn+WRQsXAjBw0GAOfN/BzZ6z24g9GLHHngC88fprzPDvTe64kLd26bVXX6Fnz5707NmT9x1yKHV1dc2es+3AQY3PF7+1uJrZs2a8++473HfvXbXORqfywnPPNT4/6OBDK25fPvQDhzce29A81ikpBQvVeNSS2+StXTrsiCM57IgjW3RORFQpN9ZSd91+KyuWLwegz6abumd9la1evZpZL80EYJOePdl51+EVn9u//5YM2X4oL8+exUszZ1BfX0+3bt2qlVVrY47kLTdee/WVxuebb96vhjnp3J56ciKzXnoRgE022YSRRx1b4xzl3/x5b7Jy5Uog9aZvafQ4bKedAaivr+f1115t9fx1BA1D6KrxqKVaX9+sVaxatYrxY9d2OtqlBZGMtZ633lrM2Afua3x9zHHHe2a1NrBw4dpJogZvQA/5IUO2b3y+YMH8VsmTtQ8u5K3DW7JkCf+87lrefHMuAD179WLf/Q6oca46n4jgjltvpj6LKHfeZTgjdt+zxrnqHApnguzXr+WzPvbbon/j8wXz57VKnjoit8mbtQNr1qxhxfLlzJ8/j5kzpvPM5Kca23/r6uo49fQz6dW7d41z2flMfOKxxiaTnj17Mer4E2qco87jvffW9nnYfAMK+R49etC7dx+WLHmPd95+uzWzZjXmQt46nJuuv67kUJ/NN+/Hh049g20HDqxBrjq3hQvmM37c2nkNRn1wNL39RavN1K+sb3zeY5NNNiiNHptswpIl7zW27XdGtR7TXg2urrfc6FLXhcWLF9Y6G51OWl8gzZwGMHzE7uy6m9cPaEuFBfOG9ozv3r17ltaKVsmTtQ+O5K3D2W7I9kTA0qVLWDB/XuMfuEULF3LrTTfw/LPTOOmU0z0MqI08NuFh5s55A4Devftw7Kjja5yjzmf16lWNz7t23bA/6w3n1dfXN3NkfuVw6noX8tbxHHjQIY0z4EUEr7/2Kk8/NYlpU9JEHjOmv8D1113LmR/7OF26uLKqmt58cy4THn6o8fWo40/wmgE1UFe39k/5qlWrKpo8qtiqrHDvrF+O0xC6/JXy/gtoHZokBm83hBM/dCpnfWIMm2TtkS/PnsXjjz5S49zl2+rVq7njlptYs2YNAHvsuTc7e/W/mmioagcam01aamVWyHfv3qNV8mTtgwt5y43thmzPaR/+aOOQlccmPNypOxFV28MPPcj8bLjVpptuxlHHHlfjHHVe3bqvjb5XrtiwNvXly5cB635h6Gyk6jxqyYW85cp2Q7ZvnNJz5cqVzJwxvcY5yqc3Xn+NJx57tPH1B084sbEWxdpenz6bNj5/++23Wnz+ihUrWLpkCQCbbda31fJltedC3nJnp512aXz+5tw5NcxJPtXX13P7rTc3rhWw1z77scOwnWqcq86tf/8tG5+/tQGLMy1etHZUypYDBrRKnjoeVe1fLbnjneVOn03XRjUrViyvYU7yadyD969TKDzz9JM88/STFZ07dcpkpk6Z3Pj63K9+wzUArWCL/mtnrJs75w1oYh35Ul55eXbj86226qyFfD65kLd259lpUxoXODnokPez5ZZbtej8hrZFgF493dO7Nb08exZPTnyi1tmwIgO23obu3buzcuVKXpk9u8Xnv5T9vnXr1o2Bgwa3cu46jlq3n1eDC3lrh8TUZ1K017t37xavYla4Gl3/Fn5BsKa9+srL7LHn3i06Z8mS9xq/tG3erx+DBw9p3LchQ71sfXV1dQwdtiPTn3+OxYsXMXfOHLbZdtuKzl28eBGvzJ4FwI4779Jph9DllQt5a3eGDduRrl27smrVKiY//RSHvP9wevSobFjPsqVLeXbqFAC6dOnSuISmtY4PHD6yxee88vLsxkJ+8OAhjD7p5FbOlQHsOnw3pj//HAD/evxRTjrltIrOe2zCw439K/bYY6+q5a+98zj5DkbSSEkhaWiZ/UOz/SPbNGPWrE169mT37I/N8mXLGHv/vRWd1zC96vJssZq99tmXnj17Vi2fZu3J8N12p98WaXGaZ6dN4cWZM5o958UZ03nm6acAGLzdEHbceZdmzrCOJreFvHVsHzh8ZONCG08/NYmHxj7QOOlKKUuXLOH6667lxZlpyFyfPpty2OFHtklezdqDLl26cNgRaz/zt918Q2MNSikzZ0znlptvaDz3qGM6+TwHVRojX+t2flfXW7vUZ9NNGX3Ch7jphn8QETz6yHhmTH+ePfbah4EDB9G7dx9Wr1nNO2+/zayXZjJl8tONE990796dMz5ylpebtU5ntxF78MrLs3n6yUksX76cf/zf39h51+GM2H1P+vXrB4jFixcy9ZlnmDlj7UqOx40+0as3UvsCuRpcyGckXQKMiYihkkYDFwD7Au8BNwDfjoi3Co7vAfw38DGgL3Az8PWImJPtHwvMjogxZa43G7g6Ii7JXgfw78Bw4MvAfRFxcsHxuwAXAscCWwELgHHAzyJiUmvcg/Zml+G7cfJpZ3DHbbewcsUKFsyf32zV/Rb9+3PqGR9pcY98s7w49rjRrFq1iqnPTCYimP78c41t9cXq6uo46phR7LX3vm2cS2srLuSLSPoGqYC/AvgdsCtwPnCwpIMiomFi6O8BY4BLgFXAF4FrgI2pIz4H2AI4F3i8IE+jgeuACcCPgPnAIODDwOOSzo2I32zEddutXYePYNttBzHh4YeYNvWZsvNyb9a3Lwe872D23e+ADV6FyywPunTpwgknncKwHXfm0UfGM3/em+sdI4kdhu3IEUcezYCtt6lBLtunWk9cUw3+a7iugaQoet+IeLlho6S7SAXsh4Frs83HA5dGxBXZMVcDm2/k9fcFdo+ImQXX3olUwH8tIn5fdPzPJZ0HXC7p6YiYsJHXb5c269uXD55wEkeP+iBz3nidhQsWsHz5Muq6dqV3r95sve22jtzbsSHbD+UbF15c62x0OruN2J3dRuzO/HlvMn/ePN599x3q6urYdLO+DBo0eJ1Joyy/XMivqxtwXmEBDxARj0qaCBzB2kJ+DvA+SYpkBbD+V+aWubuwgM9cCEwC/i6p1JeIq4Ezga+QvojkVrdu3Riy/VCGbD+01lkx6zC2GrA1Ww3YutbZaPcEdMlfIO9Cvkg9cEuZfbOAwtklvgPcC9wn6byImNLCa5Ua2VCqbX0UqWq+uQmpS9a5STobOBtguyFDSh1iZmY5ledCvmG8VbnvZg1TbdUXbFsUEfWlDgaWAY2rQETEY5L2B34BPCXpV8A3I2J10fXLKTW7y4IS2wZk17i1mfRK5jsirgSuBNh//wOimTTMzDott8l3LA2LKpebKq1P9v/bJc4pJSiKviNiOnCipKOBv5K+OJyf7V4ClJyJRVJ3oH+JXctKbJsPrIiIsU3kzczMbD15ngynYY3RcvOa7k3qFT+7YNsGRboRcT/wdeBTBZvnAsPKnHIya2sSCpWK/u8BzpRU8guZpG6SbpJ0VAuybGZmRfI4GU5uC/mIeAWYAlwkaZ2IWtLWwLeBByPivQ1JX9KBRZv6ksbUNxgPHCDpkKLzhpKq38s1CxS7lNTr/wpJ67xfWcF/JWnYXvNzWJqZWaeS5+p6gE8D9wGTJf2BFLUPBb5KiqRP2ZBEJe0ATJB0G/APUsR+AWkMe4NrgfOAuyT9EngBGEEaT/9j4POVXCsipks6K0tvP0l/JlXhDyPVHAwCTo+IVzfkZzEzs8Rt8h1MREyUtC/wLeBLpB7o84E7gO9HxOwNTHeWpGOA7wJXAfOAHwA/KTimPqtC/yFpkps+wFPAxyPiDkkVFfJZWjdJ2i/7OS4ktefPIX2B+UnWN8DMzGwduS7kIRXIZEPImjnuEtLsdeX2jyl6PQ4Y2UyabwFfyB7F+4YWvW7yK2REPAd8sqljzMxsw3icvJmZWW4pl9X1ue14Z2Zm1tk5kjczM2sHw92qwZG8mZlZTjmSNzMzo/wc6B2ZI3kzM7OcciRvZmadXhpCl79Y3pG8mZlZTjmSNzMzw23yZmZm1oE4kjczM4NchvKO5M3MzHLKkbyZmRleatbMzCy3cjiCztX1ZmZmeeVI3szMjFz2u3Mkb2ZmlleO5M3MzCCXobwjeTMzs5xyJG9mZp2eyOcQOkfyZmZmOeVI3szMTB4nb2ZmZh2II3kzMzNy2bnehbyZmRmQy1Le1fVmZmY1JunnksZKmiDpeEkjJD2YbfuNpG7ZcV+W9LCk8ZJOaS5dR/JmZmaoZkPossJ6UUSMlNQLuBd4G/hkRLwq6YvApyXdD4wEDgO6AbdKGh8RC8ul7ULezMystmYA4wEiYqmkecCqiHg1238lcA0QwP9GRAArJV0DHAHcUC5hF/JmZmZUdQjdlpImFry+MiKubHgREdPW5kEfAp4ABhfsr5fUFegPPFWQzlxgh6Yu7ELezMysuhZExAHNHSTpSFJV/LeBvxRs7wasBhYCWxWcsg0wr6k03fHOzMw6PVXxUdH1pcOBk4BvRMRKoLekbbPdnyW10z8IfCI7vjvwMWBsU+k6kjczM6shSYcBtwJPAw8qtRt8GfibpC7ANOC8rNr+MUnjs1N/HhGLmkrbhbyZmRnUbJx8RIwH+pbYdVSJY38N/LrStF1db2ZmllOO5M3MzPBSs2ZmZtaBOJI3MzMjn0vNupA3MzMjl+vTuLrezMwsrxzJm5mZtWTmmg7EkbyZmVlOOZI3MzPDQ+jMzMysA3Ek34k8+eSkBT276eVa56MVbQksqHUmbD1+X9qvvL0327dWQsJD6KyDi4itmj+q45A0sZLlG61t+X1pv/zedD4u5M3MzMhl53q3yZuZmeWVI3nryK6sdQasJL8v7Zffm6bkMJR3JG8dVkT4D1Y75Pel/fJ70/k4kjczMyOf4+RdyJuZmZHPIXSurreakTRSUkgaWuu8VEP2s42pdT46AkljJEWt82GWNy7krZGkSyTNrnU+rOUKvjA1POolvSPpJUl3Szpf0uZF52wlabaku2uV786quS+4koZm+0e2acY6OVXpUUuurjfLl88B84FuwKbAQOB9wA+B/5L07xFxc3ZsL2ArYFktMmpm1edC3ixf7omI2cUbJQ0EfgdcL+m0iLglIl6WtB0u5M2SWofdVeDqeitJ0tWSxko6QtKsrOp3SLntBef1lPRNSZMlLZP0tqSHJf2HpIo+b5LqJH1O0uOS3sseEyX9p6RNio4dL+nWJtL6u6R/ldh+iqT7s/y9l13r09m+myRdXXBsY3uxpIMl3SnpLUkLJP1V0uCitGcXtC//b1btekklP3u1RMQbwKnAPcBVkjbNdt0A/LbhOEmDJP1T0iJJcyR9X1KP4vRaeP96SPqppNckvZvds22zfSOze/W/2euG5oahRdc7M/vcvZ19rqZK+l6JJogx2fmDJE2QtELShzf6BtZYYVOapNGSHsru5RxJV5S4D2XvebZ/bOF7VOJ6sws/s9k9HSPpR5KWSLq56Phdsr8Nr0taKekNSddK2r+VboFtIEfy1pRtgH8ClwGTI+IVpe6n620HkNQfuJu0CMbVwAygN3AocDnwMUknRkTZyDErxG8C9iP94b+cVPW8P3Ax8AlJx0TE/OyUX5Ci050jYkZRWoOB04BPFmwTKaI9G7gZ+CqwBNgb+ImkUZT58ivprOzcPwL/DxgKnAs8ImmviHg7O/RsUlX4jdk9egB4vtzP3FYiYrWkzwMvAh+noHAvcAPpfn8FGEy6P6uA78IG37/vAWOAS7K0vghcAxwJTCV9+TgK+HL2HGBewfX+CJwB/Jl031cBewDnAJ/KPg/rvPfA9aTP31XZz5QLkr4BXABcQXofdgXOBw6WdFBErMoObeqeb6hzgC1In/nHC/I0GrgOmAD8iNRcNAj4MPC4pHMj4jcbcd02kdrPcxjKR4QffhARkP4gzM6eXw0EcHHRMSW3Z/vuAu4HepXYtzvwOvC7gm0js7SGFmz7HfAMsFWJNAaRCoW7CrZ1AWYCl5U4/sfAa0DXgm3fJP3RO7PE8QOAJ4F64OqC7WOyfM4H9i06ZyiwNUgRPAAADs5JREFUAvhWifQCGNNG791697KJY+8Hbsyej234WUl/wAPYr+DYbYBuG3n/ngG+WvC6B7B10blj0p+j9dK8AHgV2LHEvr7AuOwzUVf0Xj3Qlr87rf1+ZZ+rAEZmry8BVmb3YvuiYw/Jjv1Ypfe88H0vc/3ZwCVFn+UVwE5Fx+0EvAd8rkw65wFrgENrfc+be+yx174xfe7SqjyAibX6uVxdb01ZA5T6Br7edkmHA8eQIrvukjYvfJAK+EuBz0jarNTFsiraz5IKkvoSaSwBLgSOk7QbQESsAX4NjJHUtyCtXllal0UW3WTbLgJ+FhHXFV8/IuaRoo9yQ7l+FBFPFZ0zG7gFOKLMOe3RVEov0fkO6R4f1LAhIuZGRD1s1P2bA7wvi8qJiBUR8WZzmcyu923gv4GFJT4PIkWxI4BRRadf3lz6HVA34LyIWGe56Ih4FJjIup/BDbrnzbg7ImYWbbsQmAT8vfj9yd6jq4HHSDVD7ZvSOPlqPGrJhbw15ZWIKLX2dKnto4A64GlgcZnHZaQmon3KXO8Y0mfyjibSaGgLPLDgvD+RosfPFGz7FCl6KZzG8/1AH+APZa5PRLwIjC+z+59lts8Cti2zrz1aDvQs3ph9GboAuFzSbyUVL028offvO8AJwH2S9mxBPg/Nrvc7yn8eJpEK+wOLzp3Ugut0FPWkL5SlFH8GN/SeNyhVNpS6p6OAwyn//iwm1TQUvz/WRtwmb00pVcCX2z4AeJbU9tecaWW2DwAWAadXkEZjG2xELJF0JfAlSb8iRZLnkqojFxec01BovdJM2q+X2V4uElpGGq7WUWxPqvZdT0RcLmk6qa/D9Kw99S/Z7g26fxHxWNYB6xfAU9l79M2IWN1MOgOy/0eRCrimFP885T677cWa7P9ycV5d9n/hz72ooValhGWkvjBARfd8TYk0Cq3X2ZLyv/e/AMp2fs009/61CzlskXchb00q10Gu1Pb5QL+IGLsR15sPbAb8KyKWtPDcy0hNBSeT2g53zZ4Xmpf9P4TU+aycwZQuyFaUOT7oILViknoCxwL/U+6YiLhH0t7A54E/SXo3Im5iI+5fREwHTpR0NPBXUiF2fjPZbehc+XJ2fku092GBDZ+lUoUppBoMgLcLtpX7/EGJz2Az93wJJWpzACR1B/qX2FXu937FRv7eWxV1iD9MVjPlvu2X2n4PsK2ksr13JX1F0o+auN59pM/kmU2kcbqkP0uqK9weEa+Tevielz1uL1EwPEJqdz67ifSHAR8otS+ynkQd3EWkWoc/NXVQRKyOiMtJPdobRids1P3L0r0f+DqpOaU5E0iF0b81cb2DJN1S3M8j66vRns3J/t+5zP69SR0cZxds26DPX5l7PhcYVuaUk1lbk1Co3O/9mZJKBoySuikNqTyqBVmunRxOeedC3lpFRIwj9dq+SmmClXVIOok0vGZO8b6CNGaROur8rFQ7oqSDSG3si8pU9f6c1D54LPDLEukvI3Xi+pqk9b5ISBpA+Xb3Dk1p7oGLSB2lvh0R61XXZ8fsW7S5L6n39AbfP0nF7bGNaTYlq835MfDNUoWEpJ2B/wNWR8Q7zaXXnkQadjoFuCirXWkkaWtSh8MHI6LZ+1RKBfd8PHCApEOKzhtKqn6vtHr9UtKsileoaB6MrOC/kjRsr3iIYzukqv2rJVfXW2s6C7gXmCzp96ROeP2A44APkX7hL2smjS+Thg89Lukq4FHSWPvDgY+Qhul9q9SJEfGUpHGkZoMHyqT/M1L09Pds3PstpGhxH1Jv/Bsp0169AZYBIyW9BdzSRtHlKEnzSPHDJsDmpJ/3FNJ9vZR0D0o5C/ijpCuAJ0jDvE4jjWFv0KL7J2kHYIKk24B/kKLHC0hf+AotzY7/FDAtIiZm238ADAfulvRX0hfJrqSpej9JKig/Q8f0aVLt1WRJfyBF7UNJzU51pPesxSq859eSarzukvRL4AXSKIUvkr5Yfb6Sa0XE9OxzcC2wn6Q/k6rwh5FqDgYBp5f6Umltw4W8tZqImJdFBucBH83+X0L6Q3xmRFxfQRpLJR1HqhIeQ/oDXk+aTOYLpM50TXXY+jml2xMb0g/gbEl3Al8iRfxdgcnAFyLiOkk3NZfPCv2KdA8+RBpvvrKV0m3K7wueB+n+v0oaF/2HbLhVSRHxlyyqPJc08cl04NSslqbhmBbdv4iYJekY0mQ6V5Ha9X8A/KTo8veSxtj/gdSUMDE7f42kjwO3k+blP421cyNcDPw2mphcqT2LiIlZzcm3SPdyG1IBeQfw/SgxPXGF6TZ7zyOiPqsd+SHpve4DPAV8PCLuyCZNqvR6N0naL/s5LiT9/s0hfYH5yQb0p6iZWg93qwblo5nRzMxsw+25z/5xy32PVCXtYVv1nBQRB1Ql8WY4kjczs06vHfSRqwp3vDMzM8spR/JmZmaQy1DekbyZmVlOOZI3MzMjn0vNOpI3MzPLKRfyZm1E0tWSInuskvSOpHGSDmvD61+UPT9R0gJJpZacbS6dS7KJilo7f0MlrWpi/9hszHxL0x0j6b6Ny916aY6UVLzsqnVwXmrWzDbWj0lLgg4hzSg3kTTr2B5tnI/FpAlllrbxdc3arRxOXe82ebM29l5EzM2evwE8mc0WNgb4z7bKREQ8AhzcVtczs9pwJG9We/Vkv4tZlfS5kh6RNE9Sn2x7d0nfkfSipJWSXpV0uaT1pvCVtI+k27LmgLcl3Sxp16JjSlaNS9pf0o2SFkpaJukZSV9rWGVMUgDfAT6TNTucUXDugZLuyq67RNJ4ScXL/SKpq6QLJc2QtELSdEnnswF/jyR9RtJkScslvSbpp5J6SXorW2yl8NhtlVYwXCBpaXavjyiRZsX32nKkSlX1rq4366SywuhzwDHAzQW7Lgb+BhwQEe8pLat7K7Aj8AnSAjFnAVsAT0jaqiDNkcBDpHnIDwHeT5q3/j5gnYK+RH5OAsaR5qw/gbRgyUWkhYFuy/KxLWl9gGuy57dm5x4D/J20KtwBwP7AX4DfSPpqwTXqSIvYfBj4SpanLwAnAldUct8K0roM+C/SwivDgY9lebqb9ZdK3ZZ0X2aSVincO8v7rZI+XJS/iu61WUfg6nqztvVfki4gNdX1BF4CPlW4CAwwOSJ+U/D6s8D8iPhkwbaXgfGSriQtDPJVSd1I67//V0T8uuDYqZKeBB4kLfSyHkmbk5b5PSMi7irYNUvS3cCpwJqImCvpPWBZQ7ODpO6khXFOiIjnCs59XtK9wNOS/hIR84H/IBWwexQsDztb0v2kLyIVkTSK1MSxZ8FCLrOze3IZ669pPyL72QoXSfq5pJdJK+/dFxGLqfBeV5pP62hq3YLe+lzIm7WtK7JHPbCozHrhxcvkfhQ4WFKppUfrSCv0QYrae1FiOd+IGCfpoSbydSLwbFEB33DuClKEXs6hwA7Av1S6brJndszNpGj7p8Xrv0dESPo+sF71eRlnAVeVWantYtKqboVeKLUKYkT8U9LFwGhS7Uml99qsQ3Ahb9a2FkXEi80cM6/o9UDgy6RIvJT6guNmNrFu/QtNXHMIqVZhQwwEppGi/XLeLDi23NKjTeWv1DVLLpsbEYslLSzaPKuJtKaT1j1vSLeSe205I2rffl4NLuTN2p/lRa/nAP0iYr1x2ZK2Ja1BDqm3/s6SupQp6LcnrS1fyhxgvU5yFZpD+pLwckSsVwhK2i4i3i3I43BSu3mp/FXqdcr0MZDUj7SmeaGm+iMMB27Inld6r806BHe8M2t/ouj1dcBXGnraN5DUC7iL1EEM4BHSuPevFSco6VBSh7NybgNGSFovGpe0iaSPqExdfHbdd1m/ihxJ/waMbeidD1wLfD3rA1B4XBdSNXul/gr8R3EP+sz3SmzbQdK/l8jfWaQo/o5sU6X32nLI4+TNrBZ+T6oKv1/St4AXgd2B75Ii2j8DRES9pDGkHuP9SL3bu5J6yp9Dmert7Nz5WU//v0n6KfAP4D1gH1IP9jnA9cCq7LGFpK0j4s2IWCnpM8D1knqSCsoepB70XwVOjoiG4XpXkWoMHsp+lmmkKPsiYEGlNyQi7pd0NfBYls4jwADg89n/xX0dniJ9uRhGitqXZPf0ImBMRLyVHVfRvTbrKBzJm7VzWQF5AimSvIrUhvw/pKFepxRWzUfEA8DhpB7sjwMPA/uROrQ1OQ1rRFwDHEeaJGcCqQD+NvBH1i2o78vSe6rg3LtJQwEbtj8CHAQcHREPFhy3mlTIXwf8mtQO/ytSZH5+C+/LuaRC+jxgCmlY30zSvVpddPiiLG+Dsvw/lf2sows75LXkXlv+5HGcvCKKawbNzMw6l7333T/uGlu2smujDNy8x6SIOKAqiTfD1fVmZmaQy6VmXcibmZlB7XvJVYHb5M3MzHLKkbyZmRm5DOQdyZuZmeWVI3kzM+v02sNwt2pwJG9mZpZTjuTNzMzI5xA6R/JmZmY55UjezMwMctm93pG8mZlZTjmSNzMzI5eBvCN5MzOzvHIkb2ZmRj7HybuQNzMzQx5CZ2ZmZh2HI3kzM+v0RD6r6x3Jm5mZ5ZQLeTMzs5xyIW9mZpZTbpM3MzPDbfJmZmbWgTiSNzMzw0vNmpmZWQfiSN7MzEz5bJN3IW9mZp2e8Cp0ZmZm1oE4kjczM4NchvKO5M3MzHLKkbyZmRkeQmdmZmYdiCN5MzMz8jmEzpG8mZlZTjmSNzMzI5ed6x3Jm5mZ5ZUjeTMzM8hlKO9I3szMLKdcyJuZmZHGyVfjX0XXln4o6SFJ4yQd2lo/k6vrzcys0xO1G0In6WigV0QcLqkvcKOk4yKifmPTdiRvZmZWW0cDVwFExNvAOGDP1kjYkbyZmXV6Tz456e6e3bRllZLfRNLEgtdXRsSVBa/7A28WvJ4LDGiNC7uQNzOzTi8iPljDyy8EtgLmZ6+3Af7VGgm7ut7MzKy2HgDGAGRt8ocDU1ojYUfyZmZmNRQR90k6RtK4bNO3WqPTHYAiojXSMTMzs3bG1fVmZmY55ULezMwsp1zIm5mZ5ZQLeTMzs5xyIW9mZpZTLuTNzMxyyoW8mZlZTv1/XxhLP7DI+p0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word2Vec confusion matrix\n",
      "[[980 242   2]\n",
      " [232 711   2]\n",
      " [  2   5   0]]\n",
      "TFIDF confusion matrix\n",
      "[[974 249   1]\n",
      " [261 684   0]\n",
      " [  3   4   0]]\n",
      "BoW confusion matrix\n",
      "[[970 251   3]\n",
      " [274 670   1]\n",
      " [  3   4   0]]\n"
     ]
    }
   ],
   "source": [
    "# 画图\n",
    "cm_w2v = confusion_matrix(y_test_word2vec, y_predicted_word2vec)\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plot = plot_confusion_matrix(cm, classes=['Irrelevant','Disaster','Unsure'], normalize=False, title='Confusion matrix')\n",
    "plt.show()\n",
    "print(\"Word2Vec confusion matrix\")\n",
    "print(cm_w2v)\n",
    "print(\"TFIDF confusion matrix\")\n",
    "print(cm2)\n",
    "print(\"BoW confusion matrix\")\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5、基于深度学习的自然语言处理（CNN与RNN）\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 19097 unique tokens.\n",
      "(19098, 300)\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "EMBEDDING_DIM = 300\n",
    "MAX_SEQUENCE_LENGTH = 35\n",
    "VOCAB_SIZE = len(VOCAB)\n",
    "\n",
    "VALIDATION_SPLIT=.2\n",
    "tokenizer = Tokenizer(num_words=VOCAB_SIZE)\n",
    "tokenizer.fit_on_texts(clean_questions[\"text\"].tolist())\n",
    "sequences = tokenizer.texts_to_sequences(clean_questions[\"text\"].tolist())\n",
    "\n",
    "word_index = tokenizer.word_index\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "\n",
    "cnn_data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "labels = to_categorical(np.asarray(clean_questions[\"class_label\"]))\n",
    "\n",
    "indices = np.arange(cnn_data.shape[0])\n",
    "np.random.shuffle(indices)\n",
    "cnn_data = cnn_data[indices]\n",
    "labels = labels[indices]\n",
    "num_validation_samples = int(VALIDATION_SPLIT * cnn_data.shape[0])\n",
    "\n",
    "embedding_weights = np.zeros((len(word_index)+1, EMBEDDING_DIM))\n",
    "for word,index in word_index.items():\n",
    "    embedding_weights[index,:] = word2vec[word] if word in word2vec else np.random.rand(EMBEDDING_DIM)\n",
    "print(embedding_weights.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Input, Flatten, Dropout, Concatenate\n",
    "from keras.layers import Conv1D, MaxPooling1D, Embedding\n",
    "from keras.layers import LSTM, Bidirectional\n",
    "from keras.models import Model\n",
    "\n",
    "def ConvNet(embeddings, max_sequence_length, num_words, embedding_dim, labels_index, trainable=False, extra_conv=True):\n",
    "    \n",
    "    embedding_layer = Embedding(num_words,\n",
    "                            embedding_dim,\n",
    "                            weights=[embeddings],\n",
    "                            input_length=max_sequence_length,\n",
    "                            trainable=trainable)\n",
    "\n",
    "    sequence_input = Input(shape=(max_sequence_length,), dtype='int32')\n",
    "    embedded_sequences = embedding_layer(sequence_input)\n",
    "\n",
    "    # Yoon Kim model (https://arxiv.org/abs/1408.5882)\n",
    "    convs = []\n",
    "    filter_sizes = [3,4,5]\n",
    "\n",
    "    for filter_size in filter_sizes:\n",
    "        l_conv = Conv1D(filters=128, kernel_size=filter_size, activation='relu')(embedded_sequences)\n",
    "        l_pool = MaxPooling1D(pool_size=3)(l_conv)\n",
    "        convs.append(l_pool)\n",
    "\n",
    "    l_merge = Concatenate(axis=1)(convs) # 第一次删除Merge，因为Merge is not supported in Keras +2\n",
    "\n",
    "    # add a 1D convnet with global maxpooling, instead of Yoon Kim model\n",
    "    conv = Conv1D(filters=128, kernel_size=3, activation='relu')(embedded_sequences)\n",
    "    pool = MaxPooling1D(pool_size=3)(conv)\n",
    "\n",
    "    if extra_conv==True:\n",
    "        x = Dropout(0.5)(l_merge)  \n",
    "    else:\n",
    "        # Original Yoon Kim model\n",
    "        x = Dropout(0.5)(pool)\n",
    "    x = Flatten()(x)\n",
    "    x = Dense(128, activation='relu')(x)\n",
    "    #x = Dropout(0.5)(x)\n",
    "\n",
    "    preds = Dense(labels_index, activation='softmax')(x)\n",
    "\n",
    "    model = Model(sequence_input, preds)\n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['acc'])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/apple/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "# 训练网络\n",
    "x_train = cnn_data[:-num_validation_samples]\n",
    "y_train = labels[:-num_validation_samples]\n",
    "x_val = cnn_data[-num_validation_samples:]\n",
    "y_val = labels[-num_validation_samples:]\n",
    "\n",
    "model = ConvNet(embedding_weights, MAX_SEQUENCE_LENGTH, len(word_index)+1, EMBEDDING_DIM, \n",
    "                len(list(clean_questions[\"class_label\"].unique())), False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/apple/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 8701 samples, validate on 2175 samples\n",
      "Epoch 1/3\n",
      "8701/8701 [==============================] - 15s 2ms/step - loss: 0.6126 - acc: 0.6912 - val_loss: 0.5021 - val_acc: 0.7706\n",
      "Epoch 2/3\n",
      "8701/8701 [==============================] - 14s 2ms/step - loss: 0.4518 - acc: 0.7999 - val_loss: 0.4748 - val_acc: 0.7917\n",
      "Epoch 3/3\n",
      "8701/8701 [==============================] - 17s 2ms/step - loss: 0.3956 - acc: 0.8324 - val_loss: 0.4663 - val_acc: 0.8097\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a568d29e8>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 评测\n",
    "model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=3, batch_size=128)"
   ]
  }
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